Microprocessor system for the analysis of physiologic and financial datasets

ABSTRACT

A medical alarm system for processing physiologic and laboratory data in hospitals and other environments is disclosed. The alarm system provides early detection of complex pathophysiologic cascades. The alarm system detects aggregated timed patterns of inflammatory indicators, metabolic, pulse, blood pressure, oxygen saturation, and/or ventilation trends to detect expanding cascade patterns characteristic of, for example, progressive sepsis. The system may include a processor programmed to convert at least the physiologic and laboratory data of a patient into a predetermined format favorable for searching. The system may also include a processor programmed to search the formatted data to detect the progressively expanding global patterns of abnormal data characteristic of evolving sepsis and/or septic shock. The alarm system may be configured to provide an early warning for healthcare workers when a pattern indicative of progression of a complex pathophysiologic cascade is detected.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/150,842 filed May 17, 2002, which claims priority from U.S.Provisional Application No. 60/291,691 filed on May 17, 2001, claimspriority from U.S. Provisional Application No. 60/291,687 filed on May17, 2001, and is a continuation-in-part of U.S. patent application Ser.No. 09/628,655 filed Jul. 28, 2000 (now U.S. Pat. No. 6,609,016), whichis a continuation-in-part of U.S. patent application Ser. No. 09/115,226filed Jul. 14, 1998 (now U.S. Pat. No. 6,223,064), which is acontinuation-in-part of U.S. patent application Ser. No. 08/789,460filed Jan. 27, 1997 (now U.S. Pat. No. 5,891,023).

BACKGROUND

1. Field of the Invention

The present disclosure relates to an object-based system for theorganization, analysis, and recognition of complex timed processes andthe analysis and integration of time-series outputs of data sets andparticularly physiologic data sets, and to the evaluation of thefinancial and physiologic datasets and the determination ofrelationships between them.

2. Background

The analysis of time-series data is widely used to characterize thebehavior of a system. The following four general categories ofapproaches are commonly applied to achieve characterization of such asystem and these provide a general background for the presentdisclosure. The approaches are illustrative both in theirconceptualization, application, and limitations.

The first such approach represents a form of mathematical reductionismof the complexity through the application of a cascade of rules based onan anticipated relationship between the time-series output and a givenset of system mechanisms. In this approach the operative mechanisms,data set characteristics, and intruding artifact are a priori defined tothe best extent possible. Then a set of rules is applied to characterizeand analyze the data set based on predicted relationships between thedata set and the systems being characterized. Such systems often includecascading branches of decision-based algorithms, the complexity of whichincrease greatly in the presence of multiple interactive mechanisms. Thereductionism approach is severely limited by the uncertainty andcomplexity, which rapidly emerges when a cascade of rules is applied toa highly interactive data set, when the signal to noise ratio is low,and/or when multiple data sets generated by complex and dynamicallyinteractive systems are evaluated. These methods become inordinatelymore cumbersome as the complexity and number of time-series increases.In addition the subtlety of the interactive and dynamic relationshipsalong and between datasets and the variations associated with thetechnique or tools of data collection often makes the cascading rulesvery difficult to a priori define.

The failure of simplification the analysis through mathematicalreductionism to adequately characterize the complex systems generatingsuch data sets, led to the perception that this failure resulted fromspecific limitations of a particular data format (usually the timedomain format). In other words, the time-series was perceived to containsufficient information to characterize the system but, it was thought,that the recognition of this information required reformatting into adifferent mathematical representation, which emphasized other hiddencomponents which were specific for certain important systemcharacteristics. This approach is exemplified by frequency processingmethods, which reformat the time-series into frequency components, suchas its sine components or wavelets, with the hope that patterns ofspecific frequency relationships within the system will emerge to berecognized. While often uncovering considerable useful information, thisapproach remains quite limited when applied to highly complex andinteractive systems, because many complex relationships are poorlycharacterized by their frequency components, and it is often difficultto relate an output derived from frequency-based primitives to specificmechanisms operative within the system. In other words, the advantagesassociated with mathematically defined linkages between systemmechanisms and the rules based analysis provided by reductionism isreduced by the data reformatting process for the purpose of frequencybased signal processing as, for example, is provided by Fourier orwavelet transforms.

A third approach seeks to identify the patterns or relationships byrepetitively reprocessing the time-series with a set of generalcomparative rules or by statistical processing. As with the datareformatting approach, the utility of this method in isolation (asembodied in neural network based analysis), is severely limited bydissociation of the output from the complex and interactive operativemechanisms, which define the output. With such processing, the relevantscope and characterization of the relationships of the output to theactual behavior of the dynamic interactions of the system is often quitelimited. This limits the applicability of such processing inenvironments wherein the characterization of behavior of the system as afunction by the output may be as important as the actual output valuesthemselves.

A fourth approach has been to apply chaotic processing to thetime-series. Again, like conventional signal processing, this method isapplied with the expectation that some predictive pattern will emerge tobe recognized. This technique shares several of the limitations notedfor both frequency and statistical based data reformatting. In addition,as will be discussed, the application of this type of processing tophysiologic signals is limited by redundant and interactive highercontrol which greatly limits the progression of the system to a state ofuncontrolled chaotic behavior. Such systems operate in environments ofsubstantial interactive control until the development of a severedisease state, a point at which the diagnostic information provided byprocessing often has less adjective utility relevant timelyintervention.

The human physiologic system derives a large array of time-seriesoutputs, which have substantial relevance when monitored over a finitetime interval. The human can be considered the prototypic complexinteractive system. These interactions and the mechanisms defining themhave been the subject of intense research for over one hundred years andmost of this work has been performed in the time domain. For this reasonany approach toward the characterization of such a system needs toconsider the value of engaging the body of knowledge, which relates tothese mechanisms. This has been one of the reasons that reductionism haspredominated in the analysis of physiologic signals. U.S. Pat. Nos.5,765,563 to Vander Schaff, 5,803,066 to Rapoport, and 6,138,675 toBerthon-Jones show such simple cascade decision systems for processingphysiologic signals. U.S. Pat. No. 5,751,911 to Goldman shows areal-time waveform analysis system, which utilizes neural networks toperform various stages of the analysis. U.S. Pat. No. 6,144,877 toDepetrillo shows a processor-based method for determining statisticalinformation for time-series data and for detecting a biologicalcondition of a biological system from the statistical information. U.S.Pat. Nos. 5,782,240 and 5,730,144 to Katz show a system that applieschaos analyzers, which generate a time-series, vector representation ofeach monitored function and apply chaotic processing to identify certainevents. All of these systems are deficient in that they are not able toadequately organize, order and analyze the true state of dynamicinteraction operative in the generation of these signals.

Critical illness is one example of a dynamic timed process, which ispoorly characterized by the above-noted conventional methods. When humanphysiologic stability is under threat, it is maintained by a complexarray of interactive physiologic systems, which control the criticaltime dependent process of oxygen delivery to the organism. Each system(e.g. respiratory, cardiac or vascular) has multiple biochemical and/ormechanical controls, which operate together in a predictable manner tooptimize oxygen delivery under conditions of threat. For example anincreased oxygen requirement during infection causes the patient toincrease oxygen delivery by lowering lung carbon dioxide throughhyperventilation and the fall in carbon dioxide then causes thehemoglobin molecule to increase its affinity for oxygen thereby furtherenhancing oxygen delivery. In addition to the basic control of a singlesystem, other systems interact with the originally affected system toproduce a predictable pattern of response. For example, in the presenceof infection, the cardiac system interacts with the respiratory systemsuch that both the stroke volume and heart rate increase. In addition,the vascular system may respond with a reduction in arterial tone and anincrease in venous tone, thereby both reducing impedance to the flow ofoxygen to the tissues and shifting more blood into the arterialcompartment.

Each system generally also has a plurality of predicable compensationresponses to adjust for pathologic alteration or injury to the systemand these responses interact between systems. For example thedevelopment of infectious injury to the lung will result in an increasein volume of ventilated gas to compensate for the loss of functionalsurface area. This increase in ventilation can then induce a synergisticincrease in both stroke volume and heart rate.

Finally, a pathologic process altering one system will generally alsoinduce an alteration in one or more other systems and these processesare all time dependent. Sub acute or acute life threatening conditionssuch as sepsis, pulmonary embolism, or hemorrhage generally affect thesystems in cascades or predictable sequences which may have a timecourse range from as little as 20 seconds or more than 72 hours. Forexample, the brief development of airway collapse induces a fall inoxygen saturation, which then causes a compensatory hyperventilationresponse, which causes a rise in heart rate over as little as 20-30seconds. An infection, on the other hand, has a more prolonged timecourse inducing a rise in respiration rate, a rise in heart rate, andthen a progressive fall in oxygen saturation, a fall in respirationrate, and a finally a terminal fall in heart rate often over a course of48-72 hours.

It can be seen therefore that each disease process engaging the organismcauses the induction of a complex and interactive time-series ofpathophysiologic perturbation and compensation. At the onset of thedisease (such as early in the course of infection) the degree ofphysiologic change may be very slight and limited to one or twovariables. As a disease progresses both the magnitude of perturbationand the number of systems involved increases. In addition to inducing apredictable range of perturbation, a particular disease processgenerally produces a specific range of progression and pattern ofevolution as a function of injury, compensation, and system interaction.Furthermore, this multi-system complexity, which can be induced byinitial pathologic involvement of a single system, is greatlymagnified-when a plurality of pathologic processes is present.

Despite the fact that these conditions represent some of the mostimportant adversities affecting human beings, these pathologic processesare poorly characterized by even the most sophisticated of conventionalmonitors, which greatly oversimplify the processing and outputs. Perhapsthis is due to the fact that this interactive complexity overwhelmed thedevelopers of substantially all of the conventional physiologicsignal-processing methods in the same way that it overwhelms thephysicians and nurses at the bedside everyday. Hospital critical carepatient monitors have generally been applied as warning devices uponthreshold breach of specific critical parameters with the focus on thebalance between timely warning of a potentially life threateningthreshold breach and the mitigation of false alarms. However, during thepivotal time, early in the process of the evolution of critical illness,the compensatory responses limit the change in primary criticalvariables so that the user, monitoring these parameters in isolation, isoften given a false sense of security. For this reason it cannot beenough to recognize and warn of the occurrence of a respiratory arrest,or hypotension, or hypoxia, or of a particular type of cardiacarrhythmia. To truly engage and characterize the processes present, apatient monitor must have capability to properly analyze, organize, andoutput in a quickly and easily understood format the true interactivestate of critical illness. As discussed below, it is one of the purposesof the present disclosure to provide such a monitor.

SUMMARY OF DISCLOSED EMBODIMENTS

The present disclosure provides a system and method, which providecomprehensive organization and analysis of interactive complexity alongand between pluralities of time-series. One embodiment of the presentdisclosure includes an objects-based method of iterative relationalprocessing of time-series fragments or their derivatives along andbetween corresponding time-series. The system then applies an iterativecomparison process of those fragments along and between a pluralitytime-series. In this way, the relationship of a wide range ofcharacteristics of substantially any dynamic occurrence in onetime-series can be compared to the same or other characteristics ofsubstantially any dynamic occurrence along another portion of the sametime-series or any of the processed corresponding time-series.

According to the present disclosure, a first time-series is processed torender a time-series first level derived from sequential time-seriessegments of the first series. The time-series first level is stored in arelational database, object database or object-relational database. Thefirst time-series level is processed to render a second time-serieslevel derived from the sequential time-series component of the firsttime-series level and these are stored in the relational database,object database or object-relational database. Additional levels arethen derived as desired. The compositions of sequential time-series,which make up the first and second levels, are determined by thedefinitions selected for the respective segments from which each levelis derived. Each time-series fragment is represented as a time-seriesobject, and each more complex time-series object inherits the more basiccharacteristics of time-series objects from which they are derived.

The time course of sub acute and acute critical illness to point ofdeath is highly variable and can range from 24-72 hours with toxicshock, to as little as 30 seconds with neonatal apnea. The presentinventors recognized that, regardless of its time course, such apathological occurrence will have a particular “conformation”, whichaccording to the present disclosure can be represented spatially by anobject based processing system and method as a particular object ortime-series of objects, as a function of the specific progression of theinteractive components for the purpose of both processing, andanimation. The present inventors also recognized that the development ofsuch a processing system would be capable of organizing and analyzingthe inordinate degree of dynamic complexity associated with the outputfrom the biologic systems through the automatic incorporation of thesetime-series outputs into a highly organized relational, layered,object-based data structure. Finally, the inventors further recognizedthat because of the potentially rapid time course of these illnesses andthe irreversible endpoint, that patient care monitors must provide aquickly and easily understood output, which gives the medical personnela simplified and succinct analysis of these complex relationships thataccurately reflects the interactive complexity faced by the patientsphysiologic systems.

It has been suggested that the development of periodicity in a humanphysiologic system represents a simplification of that system. Thisconcept is based on the perception that human interactive physiologicsystems operate in an environment of chaos and that a partial loss ofcontrol simplifies the relationships, allowing simpler periodicrelationships to emerge. However, there is considerable reason tobelieve that this is not the case. Patients entering an environment oflower partial pressure of oxygen, as at altitude, will developperiodicity of ventilation. This does not indicate a generalsimplification of the system but rather, one proposed operativemechanism for the emergence of this new pattern is that the patternreflects the uncovering of a preexisting dynamic relationship betweentwo controllers, which now, together determine ventilation in this newenvironment. At sea level the controller responding to oxygen wassubordinate to the controller responding to carbon dioxide so that theperiodicity was absent. This simple illustration serves to demonstratethe critical linkage between patient outputs and higher control and thecriticality of comprehensively comparing dynamic relationships along andbetween signals to achieve a true picture of the operative physiology.While periodicities are, at times, clearly pathologic, their developmentin biologic systems, rather than a manifestation of simplification ofphysiological behavior, than the engagement of new, often represents theengagement of more rudimentary layers of protection of a particularorgan function or range built into the control system. This illustrationfurther demonstrates that a given physiologic signal, when monitored inisolation, may appear to exhibit totally unpredictable and chaoticbehavior, but when considered in mathematical or graphical relation (asin phase space) to a plurality of corresponding interactive signals, andto the interactive control mechanisms of those corresponding signals,the behavior of the original, chaotic appearing, signal often becomesmuch more explicable.

In an example, consider a timed plot of oxygen saturation (SPO2) underheavy sedation during sleep. This state is often associated with a lossof the maintenance of a narrow control range of ventilation during sleepand with the loss of stability of the airway so that a plot of theoxygen saturation, in the presence of such deep sedation, shows a highlyvariable pattern, which often appears grossly unpredictable, withsustained falls in oxygen saturation intermixed with rapid falls andoften seemingly random rapid corrections. However, there are definablelimits or ranges of the signal, and generally definable patterns, whichare definable within the background of a now highly variable SPO2signal. It may be temping to define this behavior statistically or by achaotic processor in the hope of defining some emerging patterns as afunction of the mathematical behavior of that signal. However, whenanalyzed with the partial pressure of CO2, the minute ventilation, and aplot of EEG activity the oxygen saturation values are seen as asubordinate signal to the airflow which is now being controlled by adysfunctional control process, which process is being salvaged by a morecoarse and rudimentary survival response mechanism. The apparentlychaotic behavior is now seen as driven by a complex but predictablesequence of a plurality of dynamic interactive relationships betweencorresponding signals and the forces impacting them. Therefore, in thepresence of a pathophysiologic process, the behavior and ranges of anygiven signal are optimally defined by the dynamic patterns of theinteractive behavior of corresponding signals and their respectivedynamic ranges.

A biologic system actually exploits the chaotic output of simplenonlinear relationships by defining control ranges, which are affectedby variations in corresponding signals. This produces a great degree indiversity of dynamic physiologic response, which is beneficial in thatit may favor survival of a particular subgroup, in the presence of acertain type of pathophysiologic threat. The present inventors notedthat, while this diversity imparts greater complexity, this complexitycan be ordered by the application of an iterative microprocessor system,which defines a given signal as a function of a range “dynamicnormality”. According to one embodiment of the present disclosure, eachsignal is defined as a function of its own dynamic range (and inrelation to a predicted control range) and as a function ofcontemporaneously relevant relationships of the dynamic ranges of othercorresponding signals (with respect to their respective control ranges).

In one embodiment, the present disclosure comprises a system and methodfor organizing and analyzing multiple time-series of parametersgenerated by a patient (as during critical illness) and outputting thisanalysis in readily understandable format. The preferred system iscapable of simultaneously processing dynamic time-series of physiologicrelationships in real time at multiple levels along each parameter andacross multiple levels of different parameters. The present embodimentsprovide this level of interactive analysis specifically to match thecomplexity occurring during a pathologic occurrence. More specificallythe present embodiments provide an analysis system and method, whichanalyses the true dynamic state of a biologic system and the interactiveprimary and compensatory perturbations defining that state. Duringhealth, the output of physiologic systems are maintained within tightvariances. As will be discussed, using the signal processing system ofthe present the extent to which the signals are held within these tightvariances are characterized as a function of their dynamic ranges ofvariance and the signals are further characterized as a function oftheir dynamic relationships along the time-series within a given signaland between a plurality of additional corresponding signals. As will belearned by the following disclosure, the optimal monitor of the humanphysiologic state during critical illness must be capable of analyzingtime-series relationships along and between a plurality signals with thesimilar degree of analytic complexity as is operative in the biologicsystems controlling the interactive responses which are inducing thosesignals, and capable of outputting an indication based on the analysisin a readily understandable format. In the preferred embodiment this isprovided as a dynamic format such as a two-dimensional orthree-dimensional object animation, the configuration of which isrelated to the analysis output. The configurations of the animationchanges with the analysis output, as this output changes over time inrelation to changes in the patient's physiologic state. The animationthereby provides a succinct and dynamic summary rendering whichorganizes the complexity of the interactive components of the output sothat they can be more readily understood and used at the bedside and forthe purpose of patient management and education of medical staffrelevant the application of time-series analysis in the assessment ofdisease. According to one embodiment of the present disclosure, theprocess proceeds by the following sequence;

-   -   Organize the multiple data streams defining the input into a        hierarchy of time-series objects in an objects based data        structure.    -   Analyze and compare of the objects along and across time-series,    -   Organize and summarize (and/or simplify) the output.    -   Animate and present the summarized output.    -   Take action based on the output.    -   Analyze and compare the new objects derived subsequent the        action.    -   Adjust the action.    -   Repeat the cycle.    -   Calculate the expense and recourse utilization related to said        output.

Using the above system, according to the present disclosure, a pluralityof time-series of physiologic signals (including timed laboratory data)of a given physiologic process (such as sepsis) can have a particularconformational representation in three-dimensional space (as is shown inFIGS. 2 a and 2 b). This spatial representation comprises a summary ofthe relational data components, as analyzed, to diagnose a specificpathophysiologic process, to determine its progression, to define itsseverity, to monitor the response to treatment, and to simplify therepresentative output for the health care worker.

Two exemplary pathophysiologic processes (airway instability and sepsis)will be discussed below. Further, exemplary patient monitoring systemsand methods for processing, organizing, analyzing, rendering andanimating output, and taking action (including additional testing ortreatment based on said determining) in accordance with presentembodiments will be disclosed.

A major factor in the development of respiratory failure is airwayinstability, which results in air-way collapse during sedation, stroke,narcotics, or stupor. As illustrated in FIGS. 5 a and 5 b, such collapseoccurs in dynamic cycles called apnea clusters affecting a range ofphysiologic signals. These apnea clusters are an example of a process,which, perhaps due to the dynamic interactive complexity of thetime-series, is not recognized by conventional hospital processors. Yetsubgroups of patients in the hospital may be at risk from this disorder.Patients with otherwise relatively stable airways may have instabilityinduced by sedation or narcotics and it is desirable for thisinstability to be recognized in real time in the hospital so that thedose can be adjusted or the drug withheld upon the recognition of thisdevelopment. Conventional patient monitors are neither configured toprovide interpretive recognition of the cluster patterns indicative ofairway and ventilation instability nor configured to provideinterpretative recognition of the relationship between apnea clusters.In fact, such monitors often apply averaging algorithms, which attenuatethe clusters. For these reasons thousands of patients each day enter andleave hospital-monitored units with unrecognized ventilation and airwayinstability.

This failure of conventional hospital based central patient monitorssuch as Agilent CMS, or the GE-Marquette Solar 8000, to automaticallydetect, and quantify obstructive sleep apnea or the cluster patternsindicative of airway instability can be seen as a major health caredeficiency associated with a failure to address a long and unsatisfiedneed. Because sleep apnea is so common, the consequence of the failureof conventional hospital monitors to routinely recognize apnea clustersmeans that the diagnosis was missed in perhaps hundreds of thousands ofpatients who unknowingly have this disorder and who have been monitoredin the hospital over the past decade. Many of these patients will neverbe diagnosed in their lifetime and will needlessly suffer with thecomplications of the disorder. For these patients, the diagnosticopportunity was missed and the health implications and risk ofcomplications associated with undiagnosed airway instability and sleepapnea will persist in this group throughout the rest of their lifesimply because it was not recognized that simple modifications andprogramming of these devices could allow automatic recognition of thiscommon disorder. A second group of patients may have a complication inthe hospital due to the failure to timely recognize obstructive sleep orairway instability. Also an important opportunity to enhance the valueof a conventional critical care monitor, to increase the efficiency ofthe diagnosis of obstructive sleep apnea, and to increase the revenuefor the critical care monitoring companies marketing has been lost.Further an opportunity to increase hospital and/or physician revenue hasbeen missed.

To understand the criticality of recognizing airway instability inreal-time it is important to consider the significance of the combinedeffect that oxygen therapy and narcotics or sedation may have in thepatient care environment in the hospital. For example, in the managementof a post-operative obese patient after upper abdominal surgery, thepatient may be at particular risk for increased airway instability inassociation with narcotic therapy in the 1st and 2nd post-operative daydue to sleep deprivation, airway edema, and sedation. Indeed, many ofthese patients have significant sleep apnea prior to admission to thehospital which is unknown to the surgeon or the anesthesiologist due tothe subtlety of symptoms. These patients, even with severe sleep apnea,are relatively safe at home because of an arousal response; however, inthe hospital, narcotics and sedatives often remove this “safety net”.The administration of post-operative narcotics can significantlyincrease airway instability and, therefore, place the patient atsubstantial risk. Many of these patients are placed onelectrocardiographic monitoring but the alarms are generally set at highand low limits. Hypoxemia induced by airway instability generally doesnot produce marked levels of tachycardia; therefore, airway instabilityis poorly identified by simple electrocardiographic monitoring withoutthe identification of specific clusters of the pulse rate. In addition,simple oximetry evaluation is also a poor method to identify airwayinstability. Conventional hospital oximeters often have averagingintervals, which attenuate the dynamic desaturations. Even when theclustered desaturations occur they are often thought to represent falsealarms because they are brief when desaturations are recognized aspotentially real this often results in the simple and often misguidedaddition of nasal oxygen. However, nasal oxygen may prolong the apneasand potentially increase functional airway instability. From amonitoring perspective, the addition of oxygen therapy can be seen topotentially hide the presence of significant airway instability byattenuation of the level of desaturation and reduction in theeffectiveness of the oximeter as a monitoring tool in the diagnosis ofthis disorder.

Oxygen and sedatives can be seen as an undesirable combination inpatients with severely unstable airways since the sedatives increase theapneas and the oxygen hides them from the oximeter. For all thesereasons, as will be shown, according to the present disclosure, it isdesirable to monitor for the specific cluster patterns, which arepresent during the administration of narcotics, or sedatives in patientswith increased risk of airway instability.

The central drive to breathe, which is suppressed by sedatives ornarcotics, basically controls two critical muscle groups: the upperairway “dilator muscles” and the diaphragm “pump muscles”. The tone ofboth these muscle groups must be coordinated. A fall in tone from thebrain controller to the airway dilators results in upper airwaycollapse. Alternatively, a fall in tone to the pump muscles causeshypoventilation.

There are two major factors which contribute to respiratory arrest inthe presence of narcotic administration and sedation. The first and mosttraditionally considered potential effect of narcotics or sedation isthe suppression of the drive to pump muscles. In this situation, airwayinstability may be less important than the reduced stimulation of thepump muscles, such as the diaphragm and chest wall, resulting ininadequate tidal volume and associated fall in minute ventilation and aprogressive rise in carbon dioxide levels. The rise in carbon dioxidelevels causes further suppression of the arousal response, therefore,potentially causing respiratory arrest. This first cause of respiratoryarrest associated with sedation or narcotics has been the primary focusof previous efforts to monitor patients postoperatively for the purposeof minimization of respiratory arrests. Both oximetry and tidal CO2monitoring have been used to attempt to identify and prevent thisdevelopment. However, in the presence of oxygen administration, oximetryis a poor indicator of ventilation. In addition, patients may have acombined cause of ventilation failure induce by the presence of bothupper airway instability and decreased diaphragm output. In particular,the rise in CO2 may increase instability of the respiratory controlsystem in the brain and, therefore potentially increase the potentialfor upper airway instability.

The second factor causing respiratory arrest due to narcotics orsedatives relates to depression of drive to upper airway dilator musclescausing a reduction in upper airway tone. This reduction in airway toneresults in dynamic airway instability and precipitates cluster cycles ofairway collapse and recovery associated with the arousal response as thepatient engages in a recurrent and cyclic process of arousal basedrescue from each airway collapse. If, despite the development ofsignificant cluster of airway collapse, the narcotic administration orsedation is continued, this can lead to further prolongation of theapneas and eventual respiratory arrest. There is, therefore, a dynamicinteraction between suppression of respiratory drive, which results inhypoventilation and suppression of respiratory drive, which results inupper airway instability. At any given time, a patient may have agreater degree of upper airway instability or a greater degree ofhypoventilation. The relative combination of these two events willdetermine the output of the monitor, with the former producing a simpletrending rise (as with end tidal CO2) or fall (as with minuteventilation or oxygen saturation) and the latter producing a clusteroutput pattern.

Unfortunately, this has been one of the major limitations of carbondioxide monitoring. The patients with significant upper airwayobstruction are also the same patients who develop significanthypoventilation. The upper airway obstruction may result in drop out ofthe nasal carbon dioxide signal due to both the upper airway obstructionon one hand, or be due to conversion from nasal to oral breathing duringa recovery from the upper airway obstruction on the other hand. Althoughbreath by breath monitoring may show evidence of apnea, conversion fromnasal to oral breathing can reduce the ability of the CO2 monitor toidentity even severe hypoventilation in association with upper airwayobstruction, especially if the signal is averaged or sampled at a lowrate. For this reason, conventional tidal CO2 monitoring when appliedwith conventional monitors may be least effective when applied topatients at greatest risk (i.e., those patients with combined upperairway instability and hypoventilation).

As described in U.S. Pat. No. 6,223,064, the underlying cyclicphysiologic process, which drives the perpetuation of a cluster ofairway closures, can be exploited to recognize upper airway instabilityin real time. The underlying cyclic process, which defines the behaviorof the unstable upper airway, is associated with precipitous chances inventilation and attendant precipitous changes in monitored parameters,which reflect and/or are induced by such ventilation changes. Forexample, cycling episodes of airway collapse and recovery producessequential precipitous changes in waveform output defining analogouscluster waveforms in the oximetry pulse tracing, the airflow amplitudetracing, the oximetry SPO2 tracing, the chest wall impedance tracing andthe EKG pulse rate or R to R interval tracing.

The use of central hospital monitors generally connected to a plurality(often 5 or more) of patients through telemetry is a standard practicein hospitals. While the identification of sleep apnea in the hospital isrelatively common, at the present time, this requires the application ofadditional monitors. The present inventors are not aware of any of thecentral patient monitors (such as those in wide use which utilizecentral telemetry), which provide the above functionality. This isinefficient, requires additional patient connections, is not automatic,and is often unavailable. According to another aspect of the presentdisclosure, the afore-referenced conventional hospital monitors areconverted and programmed to provide a measurement and count of airflowattenuation and/or oxygen desaturation and to compare that output withthe chest wall impedance to routinely identify the presence ofobstructive sleep apnea and to produce an overnight summary andformatted output for over reading for the physician which meets thestandard of the billing code in that it includes airflow, oximetry,chest impedance, and EKG or body position. This can use conventionalapnea recognition algorithms (as are well known in the art), the apnearecognition system of U.S. Pat. No. 6,223,064, or another system forrecognizing sleep apnea.

The prior art does not teach or anticipate the conversion of thesecentral hospital monitors to provide these functionalities despite themajor advantage for national heath care, which can be immediatelygained. However, the present inventors discovered and recognized thatthe addition of such functionality to central hospital monitors wouldquickly result in a profound advantage in efficiency, patient care,reduced cost, patient safety, and potentially enhances physician andhospital revenue thereby improving the method of doing the business ofdiagnosing and treating sleep apnea. The business of diagnosis of sleepapnea has long required additional equipment and would be greatlyenhanced by the conversion and programming of central hospital monitorsto provide this functionality. Moreover, the method of using theprocessor of a central hospital monitor to automatically detectobstructive sleep apnea and provide processor based interpretiveindication of obstructive output and to output a summary suitable forinterpretation to make a diagnosis of obstructive sleep apnea can resultin the automatic diagnosis of sleep apnea for hundreds of thousands ofpatients who are presently completely unaware of the presence of thisdisorder, and greatly improves the conventional method of doing thebusiness of diagnosing sleep apnea. This also allows the patientmonitoring companies, which manufacture the central hospital monitors toenter the sleep apnea diagnostic market and to exploit that entry byproviding telemetry connection of positive pressure devices to theprimary processor or secondary processor of the carried telemetry unitso that positive pressure can be adjusted by the patient monitor. Thisis an important method of doing the business of treating sleep apneasince it provides the hospital monitoring company with the potential forproprietary connectivity between the patient monitors and/or theassociated telemetry unit to the positive pressure devices therebyproviding a favorable mechanism for doing the business of sellingpositive pressure devices through enhancement of market entry and theincrease in the number of recognized cases.

According one aspect of the present disclosure, the recognition ofsequential precipitous changes can be achieved by analyzing the spatialand/or temporal relationships between at least a portion of a waveforminduced by at least a first apnea and at least a portion of a waveforminduced by at least a second apnea. This can include the recognition ofa cluster, which can compromise a high count of apneas with specifiedidentifying features which occur within a short time interval along saidwaveform (such as 3 or more apneas within about 5-10 minutes) and/or caninclude the identification of a waveform pattern defined by a closelyspaced apnea waveform or waveform clusters. Further, the recognition caninclude the identification of a spatial and/or temporal relationshipdefined by waveform clusters, which are generated by closely spacedsequential apneas due to cycling upper airway collapse and recovery.Using the above discoveries, typical standard hospital monitors can beimproved to provide automatic recognition of apnea clusters indicativeof upper airway instability and to provide an automatic visual oraudible indication of the presence of such clusters and further toprovide a visual or audible output and severity of this disorder therebyrendering the timely recognition and diagnosis of upper airwayinstability and obstructive sleep apnea as routine and automatic in thehospital as the diagnosis of other common diseases such as hypertension.

FIG. 5 a illustrates the reentry process driving the propagation ofapnea clusters. The physiologic basis for these clusters has beenpreviously described in U.S. Pat. Nos. 5,891,023 and 6,223,064 (thedisclosure of each of which is incorporated by reference as ifcompletely disclosed herein). This cycle is present when the airway isunstable but the patient is capable of arousal. In this situation, inthe sleeping or sedated patient, upon collapse of the airway, thepatient rescues herself and precipitously opens the airway to recover byhypoventilation. However, if the airway instability remains after thearousal and rescue is over, the airway collapses again, only to berescued again thereby producing a cluster of closely spaced apneas withdistinct spatial, frequency and temporal waveform relationships betweenand within apneas wherein the physiologic process reenters again andagain to produce a clustered output. According to the presentdisclosure, an apnea cluster is comprised of a plurality (two or more)of closely spaced apneas or hypopneas but the use of 3 or more apneas ispreferred. The present invention includes recognition of apnea clustersin SPO2, pulse, chest wall impedance, blood pressure, airflow (includingbut not limited to exhaled carbon dioxide and air temperature), systolictime intervals, and electrocardiograph tracings including pulse rate andR to R interval plots and timed plots of ST segment position and chestwall and/or abdominal movements. For all of these waveforms the basicunderlying cluster pattern is similar and the same basic core clusterpattern recognition system and method, according to the presentdisclosure, can be applied to recognize them.

The present disclosure further includes a system for defining thephysiologic status of a patient during critical illness based on thecomparison of a first parameter along a first monitored time intervaldefining a first timed data set to at least one other parameter along asecond time interval, defining a second timed data set. The second timeinterval corresponds to the first time interval and can actually be thefirst time interval or another time interval. The second time intervalcorresponds to the effected physiologic output of the second parameteras inclined by the output of the first parameter during the first timeinterval. For example the first time interval can be a five to fifteenminute segment of timed airflow and the time interval can be a slightlydelayed five to fifteen minute segment of timed oxygen saturationderived from the airflow which defined the dataset of the first timeinterval.

According another aspect of the present disclosure, the microprocessoridentifies changes in the second parameter that are unexpected inrelationship to the changes in the first parameter. For example, whenthe microprocessor identifies a pattern indicative of a progressive risein minute ventilation associated with a progressive fall in oxygensaturation, a textual warning can be provided indicating physiologicdivergence of the oxygen saturation and minute ventilation. For example,the term “divergent oxygen saturation” can be provided on the patientmonitor indicating that an unexpected change in oxygen saturation hasoccurred in association with the ventilation output. The occurrence ofsuch divergence is not necessarily a life threatening condition but canbe an early warning of significant life threatening conditions such aspulmonary embolism or sepsis. If the patient has an attached apparatuswhich allows the actual minute ventilation to be quantitatively measuredrather than trended then, divergence can be identified even when theoxygen saturation does not fall as defined by plotting the timed outputof ventilation indexing oximetry as by formulas discussed in the U.S.patent applications (of one of the present inventors) entitled MedicalMicroprocessor System and Method for providing, a Ventilation IndexedValue 60/201,735 and Microprocessor system for the simplified diagnosisof sleep apnea Ser. No. 09/115,226 (the disclosure of each of which isincorporated herein by reference as if completely disclosed herein).Upon the identification of divergence, the time-series of otherparameters, such as the temperature, white blood cell count and otherlab tests, can be included to identify the most likely process causingthe divergence.

One of the reasons that the identification of pathophysiologicdivergence is important is that such identification provides earlierwarning of disease. In addition, if the patient progresses to developsignificantly low levels of a given parameter, such as oxygen saturationor pulse, it is useful to be able to go back and identify whether or notthe patient experienced divergence of these parameters earlier sincethis can help identify whether it is a primary cardiac or pulmonaryprocess which is evolving and indeed the time course of the physiologicprocess is provided by both diagnostic and therapeutic. Consider, forexample, a patient experiencing significant drop in oxygen saturationand cardiac arrest. One purpose of the present disclosure is to providean output indicative of whether or not this patient experienced acardiac arrhythmia which precipitated the arrest or whether someantecedent pulmonary process occurred which caused the drop in oxygensaturation which then ultimately resulted in the cardiac arrhythmia andarrest. If the patient is being monitored by chest wall impedance,oximetry and EKG, all three parameters can be monitored for evidence ofpathophysiologic divergence. If, according to the present disclosure,the processor identifies divergence of the oxygen saturation inassociation with a significant rise in minute ventilation, thenconsideration for bedside examination, chest x-ray, arterial blood gasmeasurement can all be carried out so that the relationship betweencardiac and pulmonary compensation in this patient can be identifiedearly rather than waiting until a threshold breach occurs in one singleparameter. Since, with the use of conventional monitors, thresholdbreach of an alarm can be severely delayed or prevented by an activecompensatory mechanism, such as hyperventilation, one advantage of thepresent disclosure is that the processor can provide warning as much as4 to 8 hours earlier by identifying pathophysiologic divergence ratherthan waiting for the development of a threshold breach.

Another example of the value of monitor-based automatic divergencerecognition, according to the present disclosure is provided by apatient who has experienced a very mild breach of the alarm threshold inassociation with significant physiologic divergence, such as a patientwhose baseline oxygen saturation is 95% in association with a givenbaseline amplitude and frequency of minute ventilation as identified bythe impedance monitor. For this patient, the fall in oxygen saturationover a period of two hours from 95% to 89% might be perceived by thenurse or house officer as representing only a mild change which warrantsthe addition of simple oxygen treatment by nasal cannula but no furtherinvestigation. However, if this same change is associated with markedphysiologic divergence wherein the patient has experienced significantincrease in the amplitude and frequency of the chest impedance, themicroprocessor identification of significant pathophysiologic divergencecan give the nurse or house officer cause to consider furtherperformance of a blood gas, chest x-ray or further investigation of thisotherwise modest fall in the oxygen saturation parameter.

It is noted that excessive sedation is unlikely to produce physiologicdivergence since sedation generally results in a fall in minuteventilation, which will be associated with a fall in oxygen saturationif the patient is not receiving nasal oxygen. The lack ofpathophysiologic divergence in association with a significant fall inoxygen saturation can provide diagnostic clues to the house officer.

In a preferred embodiment, the processor system can automatically outputan indication of pathophysiologic divergence relating to timed data setsderived from sensors which measure oxygen saturation, ventilation, heartrate, plethesmographic pulse, and/or blood pressure to provide automaticcomparisons of linked parameters in real time, as will be discussed. Theindication can be provided in a two or three-dimensional graphicalformat in which the corresponding parameters are presented in summarygraphical format such as a timed two-dimensional or three-dimensionalanimation. This allows the nurse or physician to immediately recognizepathophysiologic divergence.

According to another aspect of the disclosure, the comparison of signalscan be used to define a mathematical relationship range between twoparameters and the degree of variance from that range. This approach hassubstantial advantages over the simple comparison of a given signal withitself along a time-series to determine variability with respect to thatsignal (as is described in Griffin U.S. Pat. No. 6,216,032, thedisclosure of which is incorporated by reference as if completelydisclosed herein), which has been shown to correlate loosely with adiseased or aged physiologic system. The signal variability processingmethod of the prior art, which has been widely used with pulse rate,lacks specificity since variance in a given signal may have many causes.According to the present disclosure a plurality of signals are trackedto determine if the variability is present in all of the signals, todefine the relationship between the signals with respect to thatvariability, and to determine if a particular signal (such as forexample airflow) is the primary (first) signal to vary with othersignals tracking the primary signal. For example, airway instability,sepsis, stroke, and congestive heart failure are all associated with ahigh degree of heart rate variability and this can be determined inrelation to a baseline or by other known methods, however in thepreferred embodiment the general variability of a plurality of signalsis determined and these are matched to determine if a particular signalhas a greater variability than the other signals, and more importantlythe dynamic relationship between the signals is determined to identifythe conformation of that variability. In this respect, for example, thepulse in sepsis in a neonate may show a high degree of variability byconfirming that this variability is associated with a generalmulti-parameter conformation as shown in FIGS. 2 a and 2 b (and will bediscussed in more detail) rather than a conformation of rapidlyexpanding and contracting parameters, as is typical of airwayinstability. In this way, the etiology of the pulse variability is muchbetter identified. Variability is therefore defined in relation to whichparameters are changing, whether they are changing together in aparticular category of conformation indicative of a specific diseaseprocess, and the extent to which they follow anticipated subordinatebehavior is identified. According to another aspect of the presentdisclosure, the time-series of the parameter “relationship variance” andthe time-series of the “relationship variability” are analyzed as partof the cylindrical data matrix.

Early in the state of sepsis, airflow and heart rate variability beginto develop. However early the oxygen saturation is closely linked to theairflow tracking the airflow and showing little variance near the top ofits range. As septic shock evolves, variability increases and the tightrelationship between airflow and oxygen saturation begins to breakdown.In one embodiment, this relationship is analyzed, as time-series of thecalculated variance of the airflow, variance of the heart rate, andvariance of the oxygen saturation, along with the streaming time-seriesof objects of the original measured values. Timed calculated variabilitythereby comprising components of a cylindrical data matrix of objectsanalyzed according to the methods described herein for time-seriesanalysis. Furthermore a time-series of the variance from a givenrelationship and the variability of that variance is derived and addedto the data matrix. In an example, an index of the magnitude value ofairflow in relation to the magnitude value of oxygen saturationand/heart rate is calculated for each data point (after adjusting forthe delay) and a time-series of this index is derived. Then atime-series of the calculated variability of the index is derived andadded to the data matrix. The slope or trend of the index of “airflow”and oxygen saturation will rise significantly as septic shock evolvesand this can be correlated with the slope of the variability of theindex. In comparison with septic shock in airway instability,time-series of these parameters shows a high degree of variabilitygenerally but a relatively low degree of variance of the indexedparameters associated with that variability (since despite theirprecipitous dynamic behavior, these parameters generally move togethermaintaining the basic relationships of physiologic subordinance). Inaddition to heart rate, a time-series of the plethesmographic pulse (asamplitude, ascending slope, area under the curve, etc.) variability andvariance (as with continuous blood pressure or airflow) can be derivedand incorporated with the data matrix for analysis and comparison todetermine variability and variance relationships as well as to definethe general collective conformation of the dynamic relationships of allof these parameters.

According to another aspect of the disclosure, the analysis ofsubsequent portions of a time-series can automatically be adjusted basedon the output of the analysis of preceding portions of a time-series. Inan example, with timed waveforms, such as SPO2, in clinical medicine,there are two situations: one in which motion is present wherein it iscritical to mitigate the effect of motion on the waveform and a secondsituation in which motion is not present, wherein it would be optimalnot to apply motion algorithms so that true accurate waveform can bereflected without smoothing. The application of motion algorithms on acontinuous basis results in significant smoothing of the entire waveformeven when motion is not present, thereby attenuating the optimalfidelity of the waveform and potentially hiding important short termprecipitous changes. For example, the application of these algorithmsresults in modification of the slope of the desaturation and the slopeof resaturation and affects the relative relationship between thedesaturation and resaturation slopes. One embodiment of the presentdisclosure includes a conventional system and method for detectingmotion. The system and can include the motion detection method, whichare utilized by Masimo Incorporated or Nellcor Puritan BennettIncorporated and are well known in the art. According to the presentdisclosure, the signal is processed in one of two ways. If motion isdetected the signal is processed through a motion mitigation algorithmsuch as the Masimo SET, as is known in the art. Subsequently, thissignal is processed with cluster analysis technology for the recognitionof airway instability. The cluster analysis technology is adjusted toaccount for the effect of averaging on the slopes and the potential foraveraging to attenuate mild desaturations. In the second instance, whenno motion is detected, the output is processed with a shorter averaginginterval of about 1 to 2 seconds. This produces optimal fidelity of thewaveform. This waveform is then processed for evidence of airwayinstability using cluster recognition.

According to one aspect of the disclosure, a microprocessor system isprovided for the recognition of specific dynamic patterns of interactionbetween a plurality of corresponding and related time-series, the systemcomprising a processor the processor programmed to: process a firsttime-series to produce a lower-level time-series of sequentialtime-series fragments derived from the first time-series, process thelower-level time-series to produce a higher-level time-series comprisedof sequential time-series fragments from the lower-level time-series,process a second time-series, the second time-series being related tothe first time-series, produce a second lower-level time-series ofsequential time-series fragments derived from the second time-series,and identify a dynamic pattern of interaction between the firsttime-series and the second time-series. The system can be furtherprogrammed to process the lower-level time-series of the secondtime-series to: produce a higher-level time-series derived fromsequential time-series fragments of the second lower-level time-series.The system can be programmed to process a third time-series, the thirdtime-series being related to at least one of the first and the secondtime-series, to produce a third lower-level time-series of sequentialtime-series fragments derived from said third time-series. The systemcan be programmed to process the higher-level time-series to produce acomplex-level time-series derived from sequential time-series fragmentsof said higher-level time-series. The time-series fragments of the firstand second time-series can be stored in a relational database, thefragments of the higher-level time-series can comprise objects, theobjects inheriting the characteristics of the objects of the lower-leveltime-series from which they are derived. The first and secondtime-series can comprise datasets of physiologic data points and thesystem can comprise a patient monitoring system wherein the dynamicpattern of interaction comprises pathophysiologic divergence.

In one presently preferred embodiment, the system comprises, a monitorhaving a plurality of sensors for positioning adjacent a patient and aprocessor programmed to: produce a first timed waveform based on a firstphysiologic parameter of the patient, produce a second timed waveformbased on a second physiologic parameter which is generally subordinateto the first physiologic parameter so that the second parameter normallychanges in response to changes in the first parameter, and identifypathophysiologic divergence of at least one of the first and secondphysiologic parameters in relationship to the other physiologicparameter. The system can be further programmed to output an indicationof said divergence, calculate an index of said divergence and/or providean indication based on said index. The first parameter can, for example,comprise an indication of the magnitude of timed ventilation of apatient which can, for example, be the amplitude and/or frequency of thevariation in chest wall impedance and/or the amplitude and/or frequencyof the variation in nasal pressure and/or the amplitude and frequency ofthe variation of at least one of the tidal carbon dioxide and/or thevolume of ventilation or other measurable indicator. The secondparameter can, for example, comprise a measure of oxygen saturation andcan be pulse oximetry value or other measurable indicator of arterialoxygenation such as a continuous or intermittent measurement of partialpressure of oxygen.

Another aspect of the disclosure further includes a method of monitoringa patient comprising: monitoring a patient to produce a first timedwaveform of a first physiologic parameter and a second timed waveform ofa second physiologic parameter, the second physiologic parameter beingphysiologically subordinate to the first physiologic parameter,identifying a pattern indicative of divergence of at least one of saidwaveforms in relation to a physiologically expected pattern of the oneof the other of said waveforms and outputting an indication of saiddivergence. The first timed waveform can be, for example defined by atime interval of greater than about 5-20 minutes. The first and secondtime-series can, for example, be physiologic time-series derived fromairflow and pulse oximetry. The processor can comprise a primaryprocessor, and the system can include a secondary processor and at leastone of a diagnostic and treatment device, the primary processor beingconnectable to the secondary processor, the secondary processor beingprogrammed to control at least one of the diagnostic and treatmentdevice, the secondary processor being programmed to respond to theoutput of said primary processor. The primary processor can beprogrammed to adjust the said program of said secondary processor. Thetreatment device can be, for example, an airflow delivery systemcontrolled by a secondary processor, the secondary processor beingprogrammed to recognize hypopneas, the primary processor adjusting theprogram of said secondary processor based on the identifying. In anotherembodiment the treatment device can be an automatic defibrillator. Thesecondary processor can be mounted with at least one of the treatmentand diagnostic device, the primary processor being detachable from theconnection with the secondary processor. In one embodiment, the primaryprocessor is a hospital patient monitor capable of monitoring andanalyzing a plurality of different patient related signals, whichinclude electrocardiographic signals. In an embodiment the primaryprocessor is a polysomnography monitor capable of monitoring a pluralityof different signals including encephalographic signals.

It is the purpose of the present disclosure to provide a monitor capableof organizing the complexity of the actual operative dynamicinteractions of all of the signals both with respect to the absolutevalues, the degree of relative variation, and rate of variation alongand across multiple levels of the processed output and, morespecifically, along and across multiple levels of multiple signals.

It is further the purpose of the present disclosure to organize theinteractive complexity defining the physiologic outputs generated by theaffected physiologic systems, to recognize specific types and ranges ofinteractive pathophysiologic time-series occurrences, and to analyze thecomponents and evolution of such occurrences, thereby providing a timelyoutput which reflects the true interactive, multi-system processimpacting the patient or to take automatic action base on the result ofsaid analysis.

It is the purpose of the present disclosure to provide an iterativeprocessing system and method which analyzes both waveforms and timedlaboratory data and outputs the dynamic evolution of the interactivestates of perturbation and compensation of physiologic systems inreal-time to thereby provide a device which actually monitors andrecognizes the true physiologic state of the patient.

It is the purpose of the present disclosure to provide an iterativeobject oriented waveform processing system, which can characterize,organize, and compare multiple signal levels across a plurality ofsignals by dividing each waveform level of each signal into objects fordiscretionary comparison within a relational database, object databaseor object-relational database.

It is the purpose of the present disclosure to provide a diagnosticsystem, which can convert conventional hospital-based central telemetryand hardwired monitoring systems to provide automatic processor basedrecognition of sleep apnea and airway instability and which can outputthe data sets in a summary format so that this can be over read by thephysician so that sleep apnea can be automatically and routinelydetected in a manner similar to that of other common diseases such ashypertension and diabetes.

It is the purpose of the present disclosure to provide a diagnosticsystem, which can convert conventional hospital-based central telemetryand hardwired monitoring systems to provide processor based recognitionof sleep apnea and airway instability through the recognition ofpatterns of closely spaced apneas and/or hypopneas both in real time andin overnight interpretive format.

It is the purpose of the present disclosure to provide a system, whichidentifies, maps, and links waveform clusters of apneas fromsimultaneously derived timed signals of multiple parameters includingchest wall impedance, pulse, airflow, exhaled carbon dioxide, systolictime intervals, oxygen saturation, EKG-ST segment level, and otherparameters to enhance the real-time and overnight diagnosis of sleepapnea.

It is further the purpose of the present disclosure to provide timely,real-time indication such as a warning or alarm of the presence of apneaand/or hypopnea clusters so that nurses can be aware of the presence ofa potentially dangerous instability of the upper airway during titrationof sedatives and/or narcotics.

It is further the purpose of the present disclosure to provide a systemfor the recognition of airway instability for combined cluster mappingof a timed dataset of nasal oral pressure with tidal CO2 to identifyclusters of conversion from nasal to oral breathing and to optimallyrecognize clusters indicative of airway instability in association withtidal CO2 measurement indicative of hypoventilation.

It is further the purpose of the present disclosure to identifypathophysiologic divergence of a plurality of physiologically linkedparameters along a timed waveform over an extended period of time toprovide earlier warning or to provide reinforcement of the significanceof a specific threshold breach.

Another purpose of the present disclosure to identify an anomalous trendof a first respiratory output in relation to a second respiratory outputwherein said first output is nom ally dependent on said second output toidentify divergence of said first respiratory output in relationship tothe expected trend of said first respiratory output based on the trendof said second output.

A further purpose of the present disclosure is to plot the prolongedslope of a first respiratory output in relationship to the prolongedslope of a second respiratory output and to identify divergence of saidfirst respiratory output in relation to the slope said secondrespiratory output.

It is further the purpose of the present disclosure to provide a system,which automatically triggers testing (and comparison of the output) of asecondary intermittently testing monitor based on the recognition of anadverse trend of the timed dataset output of at least one continuouslytested primary monitor.

Another purpose of the present disclosure is to provide recognition oflower airway obstruction (as with bronchospasm or chronic obstructivepulmonary disease) by exploiting the occurrence of the forced exhalationduring the hyperventilation phase of recovery intervals after and/orbetween intermittent upper airway obstruction to identify obstructiveflow patterns within the forced exhalation tracing and thereby identifylower airway obstruction superimposed on clustered upper airwayobstruction.

It is another aspect of the present disclosure to provide a system thatautomatically customizes treatment algorithms or diagnostic algorithmsbased on the analysis of waveforms of the monitored parameters.

A further aspect of the present disclosure is to provide a method ofdoing business through linking a time-series of expense and billing datato a time-series of patient related outputs and exogenous actionsapplied to the patient so that the expense of each aspect of thepatients care can be correlated with both the procedures and medicationsadministered as well as the patient output both with respect to dynamicpatterns of interaction and specific laboratory values or comparativeresults.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 a shows a three-dimensional representation of the cylindricaldata matrix comprised of corresponding, streaming, time-series ofobjects from four different timed data sets, with each of the four datasets divided into an ascending hierarchy of 3 levels in accordance withpresent embodiments.

FIG. 1 b shows a portion of FIG. 1 a curved back upon it to illustratethe flexibility of object comparison between levels and different datasets within the same time period and across different levels ofdifferent data sets at different time periods to identify a dynamicpattern of interaction between the data sets in accordance with presentembodiments.

FIG. 2 a shows a three-dimensional representation of collectiveconformation of corresponding time-series of objects of pulse (which canbe heart rate and/or pulse amplitude), oxygen saturation, airflow, chestwall movement, blood pressure, and inflammatory indicators during earlyinfection, organized in accordance with present embodiments.

FIG. 2 b shows the representation of the dynamic multi-parameterconformation of FIG. 2 a extended through the evolution of septic shockto the death point (the point of pathologic divergence of the oxygensaturation and airflow is identified along this representation) inaccordance with present embodiments.

FIG. 3 a shows a time-series of raw data points in accordance withpresent embodiments.

FIG. 3 b shows a time-series of dipole objects in accordance withpresent embodiments.

FIG. 3 c shows a time-series of a slope set of the dipole objects ofFIG. 3 b, which removes the spatial attributes of the points andhighlights relative change in accordance with present embodiments.

FIG. 3 d shows a time-series with critical boundary points from whichthe wave pattern can be segmented and the objects can be derived andassociated properties calculated in accordance with present embodiments.

FIG. 3 e shows a time-series of trend parameters calculated to providethe trend (or polarity) analysis in accordance with present embodiments.

FIG. 3 f shows one wave pattern of FIG. 3 d, which can be derived fromthe utilization of user-defined object boundaries in accordance withpresent embodiments.

FIG. 3 g shows a representation for the manipulation by the user forobject slope or duration deviation specification in accordance withpresent embodiments.

FIG. 4 shows the organization of the waveforms of FIG. 3 into ascendingobject levels in accordance with present embodiments.

FIG. 5 a shows an illustration of the complexity of the mechanismsdefining the timed interactions of physiologic systems induced by upperairway instability, which the present inventor calls an “apnea clusterreentry cycle” in accordance with present embodiments.

FIG. 5 b shows an illustration of a raw data set of a plurality ofsignals derived from the mechanism of FIG. 5 a and which may berepresented as a multi-signal three-dimensional hierarchal object asshown in FIG. 5 a in accordance with present embodiments.

FIG. 5 c shows a schematic representation of a portion of a multi-signalobject as derived from the multiple corresponding time-series of FIG. 5b with three multi-signal recover objects up to the composite objectlevel identified for additional processing in accordance with presentembodiments.

FIG. 6 a shows a three-dimensional graphical output for clinicalmonitoring for enhanced representation of the dependent and dynamicrelationships between patient variables, which the present inventorsterm the “monitoring cube” in accordance with present embodiments.

FIG. 6 b shows a two-dimensional output of the “monitoring cube” duringa normal physiologic state in accordance with present embodiments.

FIG. 6 c shows a two-dimensional output of the “monitoring cube” showingphysiologic convergence during an episode of volitional hyperventilationin accordance with present embodiments.

FIG. 6 d shows a two-dimensional output of the “monitoring cube” showingpathophysiologic divergence as with pulmonary embolism in accordancewith present embodiments.

FIG. 6 e shows a two-dimensional output of the “monitoring cube” showinga concomitant increase in blood pressure and heart rate, wherein thecube would be rotated to see which increase came first in accordancewith present embodiments.

FIG. 7 shows a schematic of a processing system for outputting and/ortaking action based on the analysis of the time-series processing inaccordance with present embodiments.

FIG. 8 shows a schematic of a monitor and automatic patient treatmentsystem in accordance with present embodiments.

FIG. 9 shows corresponding data at the raw data level of airflow andoxygen saturation wherein the subordinate saturation signal segmentdemonstrates physiologic convergence with respect to the primary airflowsignal segment in accordance with present embodiments.

FIG. 10 shows the raw data level of FIG. 9 converted to the compositelevel where the data is now comprised of a time-series of sequentialcomposite objects derived from the data sets of airflow and oxygensaturation signals in accordance with present embodiments.

FIG. 11 shows a selected composite subordinate object of oxygensaturation from FIG. 10 matched with its corresponding primary compositeobject of airflow, as they are stored as a function of dipole datasetsin the relational database, object database or object-relationaldatabase in accordance with present embodiments.

FIG. 12 shows a comparison between two data sets of airflow wherein atthe fundamental level the second data set shows evidence of expiratoryairflow delay during the recovery object, wherein the recovery object isrecognized at the composite level in accordance with presentembodiments.

FIG. 13 shows a schematic object mapping at the composite level ofcorresponding signals of airflow and oxygen saturation in accordancewith present embodiments.

FIG. 14 shows a schematic object mapping at the composite level of twosimultaneously measured parameters with a region of anticipatedcomposite objects in accordance with present embodiments.

FIG. 15 shows a schematic object mapping and scoring at the compositelevel of two simultaneously measured parameters with the region ofanticipated composite objects in accordance with present embodiments.

FIG. 16 shows a schematic of a system for automatically changing theprocessing analysis of subsequent time-series based on the analysisoutput of an earlier portion of the time-series in accordance withpresent embodiments.

FIG. 17 shows a schematic of a system for customizing a CPAPauto-titration algorithm based on the analysis of multiple correspondingsignals in accordance with present embodiments.

FIG. 18 shows a schematic system for comparing multiple signals andacting on the output of the comparison in accordance with presentembodiments.

DETAILED DESCRIPTION

The digital object processing system, according to the presentdisclosure, functions to provide multidimensional waveform objectrecognition both with respect to a single signal and multiple signals.Using this method, objects are identified and then compared and definedby, and with, objects from different levels and from different signals.FIG. 1 a provides a representation of one presently preferred relationaldata processing structure of multiple time-series in accordance withpresent embodiments. As this representation shows, a plurality oftime-series of objects are organized into different correspondingstreams of objects, which can be conceptually represented as acylindrical matrix of processed, analyzed, and objectified data 1 withtime defining the axis along the length of the cylinder 1. In thisexample the cylinder 1 is comprised of the four time-series streams ofprocessed objects each stream having three levels and all of thetime-series and their respective levels are matched and stored togetherin a relational database, object database or object-relational database.Each streaming time-series of objects as from a single signal or source(e.g. airflow or oximetry, as in a matrix of physiologic signals) isrepresented in the main cylinder 1 by a smaller cylinder (2,3,4,5) andeach of these smaller cylinders is comprised of a grouping of ascendinglevels of time-series of streaming objects (6,7,8) with the higherlevels being derived from the level below it. The streaming objects ineach ascending time-series level are more complex with each new level,and these more complex objects contain the simpler objects of the lowerlevels as will be described.

FIG. 1 b shows a cut section 9 of the cylindrical data matrix of FIG. 1a curved back upon itself to illustrate the one important advantage oforganizing the data in this way in that each object from each groupingcan be readily compared and matched to other objects along the groupingand can further be compared and matched to other objects from each othergrouping. Furthermore, an object from one level of one signal at onetime can be readily compared to an object from another level of adifferent signal at a different time. The time-series of streamingobjects in FIG. 1 b are airflow, SPO2, pulse, and a series of exogenousactions. This is a typical data structure, which would be used inaccordance with present embodiments to monitor a patient at risk forsudden infant death syndrome and this will be discussed below in moredetail.

Using this data structure, highly complex patterns and subtlerelationships between interactive and interdependent streams of objectscan be readily defined by searching the matched object streams as willbe discussed. This allows for the recognition of the dynamic patterninteraction or conformation of the matrix of analyzed streaminginteractive objects. FIG. 2 a provides an illustration of oneconformation of a collection of analyzed time-series during earlysepsis. This is progressed through septic shock to the death point inFIG. 2 b. Each particular expected conformation will be defined by thespecific parameters chosen and the manner in which they are analyzed. Inan extension of the example, a time-series of expenditures would reflecta significant increase in the slope of resource (as financial or otherresources), which begins at a recognition point. If no recognition pointoccurs (i.e. the patient dies without the condition being diagnosed) theresource object time-series have a flat or even decreasing slope. Therecognition of a specific dynamic pattern of interaction occurrencefalling within a specified range is used to determine the presence andseverity of a specific of a biologic or physical process, and itscorrelation with a time-series of resource allocation (such as timedexpenditures) and a time-series of exogenous actions (such aspharmaceutical therapy or surgery) can be used to determine the cost andcauses of a given dynamic pattern of interaction and to better definethe efficacy of intervention. The conformation of FIGS. 2 a and 2 b canbe seen as comprising a progressive expansion, evolving to divergence ofthe parameters and eventual precipitous collapse. This can be readilycontrasted with the conformation of the cylindrical analyzed data matrixderived from the same analysis of the same time-series grouping duringthe state of evolving airway instability associated with excessivesequential or continuously infused dosing of sedation or narcotics. Inthis case the pattern is one of precipitous, cyclic, and convergentexpansion and contraction with eventual terminal contraction.

The following discussion presents one embodiment of the presentdisclosure for application to the patient care environment to achieveorganization and analysis of physiologic data and particularlyphysiologic signals and timed data sets of laboratory data from patientsduring a specific time period such as a hospitalization or perioperativeperiod.

The interaction of physiologic signals and laboratory data isparticularly complex, and requires a widely varied analysis to achievecomprehensive recognition of the many dynamic patterns of interactionindicative of potential threatening pathophysiologic events. This widevariation is due, in part, to the remarkable variation in both patientand disease related factors. Such analysis is best performed inreal-time to provide timely intervention. To accomplish this level oforganization and DPI identification through multiple levels of each dataset or waveform and then across multiple levels of multiple data sets orwaveforms, the system processes and orders all of the datasets from eachsystem of the patient into a cylindrical matrix with each of the smallercylinders containing the levels in a specific ascending fashion. Anillustrative example of one preferred method sequence for organizing thedata set of a single smaller cylinder (comprised of a single signal ofairflow) is shown in FIGS. 3 a-3 g.

According to this method, the processor first derives from a time-seriesof raw data points (FIG. 3 a) a series of dipole objects with theirassociated polarities and slopes (FIG. 3 b). As shown in FIG. 3 c thesedipoles can be represented as a slope set which removes the spatialattributes of the points and highlights relative change. As shown inFIG. 3 c, various boundary types can be used to separate the dipolesinto composite sequential objects and the figure shows threeillustrative boundary types: pattern limits, inflection points, andpolarity changes. As shown in FIG. 3 d, the system now has the criticalboundary points from which the wave pattern can be segmented and thecomposite objects can be derived and associated properties calculated.Although this is represented here as linear segments, each compositeobject is actually comprised of the original set of dipoles so that theuser can choose to consider it a straight segment with one slope or acurved segment defined by the entire slope set of the segmented object.FIG. 3 e shows how the “trend” composite objects can be identified toprovide a simplified linear trend (or polarity) analysis.

Though the “trend” object set is very useful as shown in FIG. 3 e thetime-series can be segmented into other composite objects derived fromthe utilization of more or different user-defined boundary types. Thiscan be useful even if the curved shapes can be analyzed in the simplertrend analysis because the selection of object boundaries at specificranges or deflections helps to organize the objects as a direct functionof changes in the physiologic output. In the example below, all threeboundary types are employed to derive a wave pattern wire frame. Thewire frame provides a simplified and very manageable view of the patternand has boundary attributes that can be vary useful in waveform patternsearching. This type of object segmentation can be shown (FIG. 3 f) as aset of object slopes with associated durations with the spatialrelationships removed. As is shown in FIG. 3 g, this provides arepresentation for the manipulation by the user for object slope orduration deviation specification. Such deviations may be specifiedspecifically to individual segment objects or may be globallydesignated. Deviations may or may not be designated symmetrically.Multiple deviations can be specified per segment with scoring attributes(weighted deviations) to provide even more flexibility to the user tosearch for and correlate derived patterns. In some illustratedembodiments, figures may show specified deviations per segment (but notweighted deviations) for slope and duration.

In the above exemplary manner, the time-series can be organized with itsassociated objects and user-specified deviations, all of which arestored and categorized in a relational database, object database orobject-relational database. Also as will be discussed, once processed,portions of such a time-series can then be applied as target searchobjects to other waveforms to search for similar objects and to scoretheir similarity.

In accordance with present embodiments, those skilled in the art willrecognize that complex curved shape variations can be specified in asimilar way through the selection of specific ranges in variations ofthe dipole slope data set (FIG. 3 c) defining the ranges of the curvedtarget search object. (It should be noted that while the dipole setshown appears linearized, in fact, it can be seen that the dipoles cancontain all of the information in the data points so that any curvepresent in the original raw data can be reproduced.) It is cumbersome toinput such ranges for each dipole so this can be provided by specifyinga curved shape and then moving a pointer adjacent a curved shape toidentify a range of shapes defining a curved target search object.

FIG. 4 illustrates the ascending object processing levels according tothe present disclosure, which are next applied to order the objects. Inthe preferred embodiment, these levels are defined for each signal andcomparisons can be made across different levels between differentsignals. The first level is comprised of the raw data set. The data fromthis first level are then converted by the processor into a sequence offundamental objects called dipoles to form the second (fundamentalobject) level. All of the objects, which will ultimately define complexmulti-signal objects, are comprised of these sequential fundamentalobjects having the simple characteristics of slope polarity, andduration. At this level, the dipoles can be processed to achieve a “bestfit” dipole matching of two or more signals (as will be discussed) andare used render the next level, called the “composite object level”.

The composite object level is comprised of sequential and overlappingcomposite objects, which are composed of a specific sequence of slopedipoles as defined by selected search criteria. Each of these compositeobjects has similar primary characteristics of a slope duration, andpolarity to the fundamental objects. However, for the composite objects,the characteristic of slope can comprise a time-series characteristicgiven as a slope dataset. The composite object level also has thecharacteristic of “intervening interval time-series” defined by atime-series of the intervals between the recognized or selectedcomposite objects. At this level, a wide range of discretionary indexcharacteristics can be derived from the comparison of basiccharacteristics of composite objects. Examples of such indexcharacteristics include: a “shape characteristic” as derived from anyspecified portion of the slope dataset of the object, a “positionalcharacteristic” as derived from, for example, the value of the lowest orhighest points of the object, or a “dimensional value characteristic” asderived by calculating the absolute difference between specified datapoints such as the value of the lowest and the highest values of theobject, or a “frequency characteristic” such as may be derived fromperforming a Fourier transform on the slope dataset of the object.

The next analysis level is called the “complex object level”. In thislevel, each sequential complex object comprises a plurality of compositeobjects meeting specific criteria. A complex object has the samecategories of primary characteristics and derived index characteristicsas a composite object. A complex object also has the additionalcharacteristics of “composite object frequency” or “composite objectorder” which can be used as search criteria defined by a selectedfrequency or order of composite object types, which are specified asdefining a given complex object. A complex object also has additionalhigher-level characteristics defined by the time-series of the shapes,dimensional values, and positional characteristics of its componentcomposite objects. As described for the composite objects, similar indexcharacteristics of the complex objects can be derived from thesecharacteristics for example: a “shape characteristic” derived from themean rate of change along the dataset of the mean slopes of compositeobjects. Alternatively, characteristics or index characteristics may becombined with others. For example, a shape characteristic may becombined with a frequency characteristic to provide a time-series of amathematical index of the slopes and the frequencies of the compositeobjects.

The next level, termed the “global objects level” is then derived fromthe time-series of complex objects. At this level, globalcharacteristics are derived from the time-series datasets of complexobjects (and all of their characteristics). At the global objects level,the processor can identify general specific patterns over many hours oftime. An example of one specific pattern which is readily recognizableat this level would be a regular monotonous frequency of occurrence ofone substantially complex object comprised of composite objects havingalternating polarities, each with progressively rising or falling slopedatasets. This pattern is typical of Cheyene-Stokes Respirations and isdistinctly different from the pattern typical of upper airwayinstability at this global object level. Additional higher levels can beprovided if desired as by a “comprehensive objects level” (not shown)which can include multiple overnight studies wherein a comprehensiveobject is comprised of a dataset of “global objects”.

While FIG. 3 b and FIG. 4 illustrate the levels of object derivations ofa ventilation signal, in another example a similar hierarchicalarchitecture can be derived for the timed data set of the pulse waveform(as from an arterial pressure monitor or the plethesmographic pulse).Here the fundamental level is provided by the pulse tracing itself andincludes all the characteristics such as ascending and descending slope,amplitude, frequency, etc. This signal also includes the characteristicof pulse area (which, if applied to a precise signal such as the flowplot through the descending aorta, is analogous to tidal volume in thefundamental minute ventilation plot). When the pulse signal isplethesmographic, it is analogous to a less precise signal ofventilation such as nasal pressure or thermister derived airflow. Withthese less precise measurements, because the absolute values are notreliable indicators of cardiac output or minute ventilation, the complexspatial relationships along and between signals become more importantthan any absolute value of components of the signal (such as absoluteamplitude of the ascending pulse or inspiration curve). In other word,the mathematical processing of multiple signals that are simply relatedto physiologic parameters (but are not a true measurement of thoseparameters) is best achieved by analyzing the complex spatialrelationships along and between those signals. To achieve this purpose,according to the present disclosure, as with ventilation, the pulsesignal is organized into a similar multi-level hierarchy of overlappingtime-series of objects. Subsequently these are combined and comparedwith the processed objects of respiration to derive a unified objecttime-series defined by multiple corresponding data sets.

FIG. 5 a shows an exemplary pathophysiologic process associated with acharacteristic dynamic pattern of interaction. As discussed previously,this cyclic process is induced by upper airway instability. FIG. 5 bshows four corresponding signals derived from monitoring differentoutputs of the patient during a time interval wherein the dynamicprocess of FIG. 5 a is operative. The basic signals shown are Pulse,Chest Wall Impedance, Airflow, and Oxygen Saturation (SPO2). Accordingto the present disclosure, these signals are processed into time-seriesfragments (as objects) and organized into the object levels aspreviously discussed. For the purpose of organizing and analyzingcomplex interactions between these corresponding and/or simultaneouslyderived signals, the same basic ascending process is applied to eachsignal. As shown in FIG. 5 c these streaming objects, many of whichoverlap, project along a three-dimensional time-series comprised ofmultiple levels of a plurality of corresponding signals. A “multi-signalobject” is comprised of at least one object from a first signal and atleast one object from another signal. The multi-signal object of FIG. 5c has the primary and index characteristics derived from each componentsignal and from the spatial, temporal, and frequency relationshipsbetween the component signals. As illustrated, the objects defining amulti-signal object can include those from analogous or non-analogouslevels. With this approach even complex and subtle dynamic patterns ofinteraction can be recognized.

This type of representation is too complex for presentation to hospitalpersonnel but is preferred for the purpose of general representation ofthe data organization because, at this level of complexity, a completerepresentation of the time-series does not lend itself well to atwo-dimensional graphical (and in some cases a three dimensional)representation. Along the time-series of sequential multi-signalobjects, the spatial characteristics of these multi-signal objectschange as a function of a plurality of interactive and differentcharacteristics derived from the different signals.

The mathematical power of this approach to characterize the achievedorganization of the complexity of the timed behavior of a physiologicsystem is illustrated by the application of this method to characterizethe codependent behavior of ventilation and arterial oxygen saturationand plethesmographic pulse. While these variables are codependent inthat a chance in one variable generally causes a change in the othertwo. They are also each affected differently by different pathologicinsults and different preexisting pathologic changes. For example, themulti-signal objects comprising a time-series of ventilation andarterial oxygen saturation and plethesmographic pulse in a sedated50-year-old obese smoker with asthma and sleep apnea are very differentthan those of a sleeping 50 year-old patient with Cheyene StokesRespiration and severe left ventricular dysfunction. These differencesare poorly organized or represented by any collection of two-dimensionalgraphical and/or mathematical representations. Despite this, throughoutthis disclosure, many of the signal interactions (such as those relatingto pathophysiologic divergence) will be discussed as a function of asimplified two-dimensional component representation for clarity based onolder standards of mathematical thought. However, it is one of theexpress purposes of the present disclosure to provide a much moremathematical robust system for the organization and analysis of thecomplex mathematical interactions of biologic systems through theconstruction of time-series sets of multidimensional and overlappingobjects.

To illustrate the complexity ordered by this approach, consider thecomponents of just one of the three simple recovery objects shown inFIGS. 5 b and 5 c. This single recovery object includes the followingexemplary characteristics, each of which may have clinical relevancewhen considered in relation to the timing and characteristics of otherobjects;

-   -   1. Amplitude, slope, and shape of the oxygen saturation rise        event at the composite level.    -   2. Amplitude, slope, and shape of the ventilation rise event at        the composite level which contains the following characteristics        at the fundamental level;        -   Amplitude, slope, and shape of the inspiration rise object.        -   Amplitude, slope, and shape of the expiration fall object.        -   Frequency and slope dataset of the breath to breath interval            of tidal breathing objects.        -   Frequency and slope data sets of the amplitude, slope, and            shape of the pulse rise and fall events.    -   3. Amplitude, slope, and shape of the pulse rise event at the        composite level which contains the following exemplary        characteristics at the fundamental level;        -   Amplitude, slope, and shape of the plethesmographic pulse            rise event.        -   Amplitude, slope, and shape of the plethesmographic pulse            fall event.        -   Frequency and slope datasets of beat-to-beat interval of the            pulse rate.        -   Frequency and slope data set of the amplitude, slope, and            shape of the pulse rise and fall events.

As is readily apparent, it is not possible for a health care worker totimely evaluate the values or relationships of even a modest traction ofthese parameters. For this reason, the output based on the analysis ofthese time-series of objects are presented in a succinct andinterpretive format as will be discussed.

FIGS. 6 a-6 d shows one example of a method for animation of thesummarized relationships between multiple interacting objects on thehospital monitor display. Such an animation can be shown as a small iconnext to the real-time numeric values typically displayed on presentmonitors. Once the baseline is established for a patient either forexample as the patient's baseline settings for a selected or steadystate time period (of for example 10-15 minutes) or by a selected orcalculated set of normal ranges, this is illustrated as a square. (Thepatient may initially have parameters out of the normal ranges and neverexhibit a square output). After the square for this patient isestablished, the cube is built from the evolving time-series of theseparameters. A given region of the cube can be enlarged or reduced as theparticular value monitored increases or decreases respectively. Therelationship between these variables can be readily seen even if theyremain within the normal range. The computer can flag with a redindicator a cube that is showing pathophysiologic divergence whencompared with the baseline values even though none of the values are ata typical alarm threshold. If other abnormalities (such as thedevelopment of pulse irregularity or a particular arrhythmia or STsegment change), this can be flagged on the cube so that the onset ofthese events can be considered in relation to other events. If preferredthe time-series components of the cube and their relationships tooccurrences on other monitored time-series can be provided in atwo-dimensional timeline.

Using this approach the time-series relationships of multiplephysiologic events can be characterized on the screen with a smalldynamic animated icon in a succinct and easily understood way. There aremany other alternative ways to animate a summary of the dynamicrelationships and some of these will be discussed later in thedisclosure.

One of the longstanding problems associated with the comparison ofoutputs of multiple sensors to derive simultaneous multiple time-seriesoutputs for the detection of pathophysiologic change is that theaccuracy and/or output of each sensor may be affected by differentphysiologic mechanisms in different ways. Because of this, the value ofmatching an absolute value of one measurement to an absolute value ofanother measurement is degraded. This is particularly true if themeasurement technique or either of the values is imprecise. For example,when minute ventilation is measured by a precise method such as apneumotachometer, then the relationship between the absolute values ofthe minute ventilation and the oxygen saturation are particularlyrelevant. However, if minute ventilation is being trended as by nasalthermister or nasal pressure monitoring or by chest wall impedance thenthe absolute values become much less useful. However, according to oneaspect of the present disclosure, the application of the slope dipolemethod, the relationship between a plurality of simultaneously derivedsignals can be determined independent of the relationships of theabsolute values of the signals. In this way, simultaneously derivedsignals can be identified as having convergence consistent withphysiologic subordination or divergent shapes consistent with thedevelopment of a pathologic relationship or inaccurate data acquisition.

As noted, with physiologically linked signals a specific occurrence ormagnitude of change in one signal in relationship to such a change inanother signal may be more important and much more reproducible than theabsolute value relationships of the respective signals. For this reason,the slope dipole method provides an important advantage to integratesuch signals. Using this signal integration method, two simultaneouslyacquired physiologic linked signals are compared by the microprocessorover corresponding intervals by matching the respective slope dipolesbetween the signals. Although the exact delay between the signals maynot be known, the processor can identify this by identifying the bestmatch between the dipole sets. In the preferred embodiment, this “bestmatch” is constrained by preset limits. For example, with respect toventilation and oximetry, a preset limit could be provided in the rangeof 10-40 seconds although other limits could be used depending on thehardware, probe site and averaging, intervals chosen. After the bestmatch is identified, the relationships between the signals are compared(for example, the processor can compare the slope dipole time-series ofoxygen saturation to the slope dipole time-series of an index of themagnitude of ventilation). In this preferred embodiment, each slopedipole is compared. It is considered preferable that the dipoles of eachrespective parameter relate to a similar duration (for example. 1-4seconds). With respect to airflow, calculation of the magnitude value ofairflow may require sampling at a frequency of 25 hertz or higher,however, the sampling frequency of the secondary plot of the magnitudevalue of the index can, for example, be averaged in a range of one hertzto match the averaging interval of the data set of oxygen saturation.Once the signals have been sufficiently matched at the dipole level,they can be further matched at the composite level. According to thepresent disclosure, most object matching across different signals isperformed at the fundamental level or higher, however timing matchingcan be performed at the dipole level and this can be combined withhigher level matching to optimize a timing match. FIGS. 9, 10, and 11,show schematic mapping of matched clusters of airway instability (of thetype shown in FIG. 5 b) where clusters are recognized and theircomponents matched at the composite object level. When the objects arematched, the baseline range relationship between the signals can bedetermined. This baseline range relationship can be a magnitude valuerelationship or a slope relationship. The signals can then be monitoredfor variance from this baseline range, which can indicate pathology orsignal inaccuracy. The variance from baseline can be, for example, anincrease in the relative value of ventilation in relation to theoximetry value or a greater rate of fall in oxygen saturation inrelation to the duration and/or slope of fall of ventilation. In anotherexample, the variance can include a change from the baseline delaybetween delta points along the signals.

With multiple processed signals as defined above, the user, which can bethe program developer, can then follow the following to complete theprocess of searching for a specific pattern of relationships between thesignals.

-   -   1. Specify a search wave pattern.    -   2. Analyze and divide the search pattern into objects.    -   3. Input the allowed deviation (if any) from the search pattern        or the objects comprising it.    -   4. Input additional required relationships (if any) to other        objects in the target waveform.    -   5. Apply the search pattern or selected component objects        thereof to a target waveform.

Various methods of identification may be employed to provide a wavepattern to the system. Users may:

-   -   Choose from a menu of optional patterns.    -   Select dimensional ranges for sequential related patterns of        ascending complexity.    -   Draw a wave pattern within the system with a pointing or pen        device.    -   Provide a scanned waveform.    -   Provide a data feed from another system.    -   Describe the pattern in natural language.    -   Type in a set of points.    -   Highlight a sub-section of another waveform within the system.

The system can be automated such that such a search is automaticallyapplied once the criteria are established. Also the method ofidentification of the search pattern can be preset. For example theoccurrence of a specific sequence of objects can be used as a trigger toselect a region (which can be one of those objects) as the specifiedsearch pattern, the processor can automatically search for other suchpatterns in the rest of the study. The result of any of these inputswould be a set of points with or without a reference coordinate systemdefinition as shown in FIGS. 3 a-3 g.

The system now begins its analysis of the target set of points to derivea series of object sets. These sets will be used to identify keyproperties of the wave pattern. These objects (and their boundaries)will provide a set of attributes which are most likely to be significantin the wave pattern and that can be acted upon in the following ways:

-   -   To provide parameters on which sets of rules may be applied for        the identification of expected conditions.    -   To provide parameters that can be associated with specifically        allowable deviations and/or a globally applied deviations.    -   To provide parameters than can be used to score the relative        similarity of patterns within the target waveform.

Using this method, a search can be carried out for specificpathophysiologic anomalies. This can be carried out routinely by thesoftware or on demand.

One example of the clinical utility of the application of the objectprocessing and recognition system to physiologic signals is provided byidentification of upper airway instability. As discussed in theaforementioned patents and application, events associated with airwayinstability are precipitous. In particular, the airway closure isprecipitous and results in a rapid fall in ventilation and oxygensaturation. Also the subsequent airway opening airway is precipitous,and because ventilation drive has risen during closure, the resultingventilation flow rate (as represented by a measurement of airflowdeflection amplitude) rises rapidly associated with recovery. Also,after the period of high flow rate associated with the recovery the flowrate precipitously declines when the chemoreceptors of the brain senseventilation overshoot. In this way, along a single tracing of timedairflow deflection amplitude, three predictable precipitous relativelylinear and unidirectional waveform deflections changes have occurred ina particular sequence in a manner analogous to the tracing of the SPO2or pulse rate. Subsequent to this, the unstable airway attain closessuddenly propagating the cluster of cycles in all of these waveforms.

As noted above, a hallmark of airway instability is a particular clustertimed sequence of precipitous, unidirectional changes in the timed dataset. For this reason, the first composite object to be recognized isdefined by a precipitous unidirectional change in timed output of one ofthe above parameters. The system then recognizes along the fundamentalsequential unipolar composite objects and builds the composite levelcomprised of time-series of these composite objects. One embodiment usesthe following method to accomplish this task. A unipolar “declineobject” is a set of consecutive points over which the parameter level ofthe patient is substantially continually falling. A unipolar “riseobject” is a set of consecutive points over which the parameter issubstantially continually increasing. A “negative pattern” is a declinetogether with a rise object wherein the rise follows the decline withina predetermined interval. A “positive pattern” is a rise together with adecline wherein the decline follows the rise within a predeterminedinterval. How closely these composite objects can follow each other is aspecifiable parameter. At the complex object level, a cluster is a setof consecutive positive or negative patterns that appear close together.How closely these patterns must follow each other to qualify, as acluster is a specifiable parameter. (Typical ranges for these parametershave been discussed in the aforementioned patents).

When applied, the digital pattern program proceeds in several phases. Inthe first phase, decline and rise objects are identified. In the secondphase, negative and positive patterns are identified. In the thirdphase, clusters of negative and/or positive patterns are identified inthe fourth phase of the relationship between the events and patterns iscalculated and outputted. In the fifth phase a diagnosis and severityindexing of airway or ventilation instability or sleep/sedation apnea ismade, in the sixth phase a textual alarm or signal is outputted and/ortreatment is automatically modified to eliminate cluster, then theprocess is then repeated with each addition to the dataset in real-timeor with stored timed datasets.

One system in accordance with present embodiments applies either alinear or iterative dipole slope approach to the recognition of waveformevents. Since the events associated with airway collapse and recoveryare generally precipitous and unipolar, the linear method suffices forthe recognition and characterization of these nonlinear waves. However,the iterative dipole slope approach is particularly versatile and usefulin situations wherein the user would like an option to select theautomatic identification of a specific range of nonlinear or morecomplex waves. Using the iterative dipole slope method, the user canselect specific consecutive sets of points from reference cases along awaveform as by sliding the pointer over a specific waveform region.Alternatively, the user can draw the desired target waveform on a scaledgrid. The user can also input or draw range limits thereby specifying anobject or set of objects for the microprocessor to recognize along theremainder of the waveform or along other waveforms. Alternatively, theprocessor can automatically select a set of objects based onpre-selected criteria (as will be discussed). Since the iterative dipoleprocess output is shape (including frequency and amplitude) dependentbut is not necessarily point dependent, it is highly suited to functionas a versatile and discretionary engine for performing waveform patternsearches. According to the present invention, the waveform can besearched by selecting and applying objects to function as BooleanOperators to search a waveform. The user can specify whether theseobjects are required in the same order. Recognized object sequencesalong the waveform can be scored to show the degree of match with theselected range. If desired (as for research analysis of waveformbehavior) anomalies within objects or occurring in one or more of aplurality of simultaneously processed tracings can be identified andstored for analysis.

For the purpose of mathematically defining the presently preferredobject system, according to the present disclosure, for recognition ofdigital object patterns let o₁, o₂, . . . , o_(m) be the original datapoints. The data can be converted to a smoother data set, x_(i), x₂, . .. , x_(n), by using a moving n average of the data points as a 1-4second average for cluster recognition or as a 15-30 second average forthe identification of a pathophysiologic divergence. For the sake ofclarity of presentation, assume that x, is the average of the originaldata points for the i^(th) second. A dipole is defined to be a pair ofconsecutive data points. Let d_(i)=(x_(i),x_(i)+1) be the i^(th) dipole,for i=1, 2, . . . , n−1. The polarity, say p_(i) of the i^(th) dipole isthe sign of x_(i)+1−xi, (i.e. p_(i)=1 if x_(i+1)>x_(i), p_(i)=0 ifx_(i+1)=x_(i), and p _(i)=−1 if x_(i+1)<x_(i)). For the purpose ofautomatic recognition of user specified, more complex nonlinearwaveforms, the data can be converted to a set of dipole slopes, z₁, z₂,. . . , z_(n)[[,]]. Let z_(i)=(x_(i+1)−x_(i),) be the i^(th) dipoleslope, for i=1, 2, . . . , n−1.

To recognize a decline event by applying the iterative slope dipolemethod according to the present disclosure, let {z₁, z₂, . . . , z_(n)}be a set of consecutive dipole slopes. Then {z₁, z₂, . . . , z_(n)} is adecline if it satisfies the following conditions:

-   -   1. z₁, z₂, . . . , z_(n) are less than zero, i.e., the parameter        level of the patient is continually falling over the set of        dipole slopes. (This condition will be partially relaxed to        adjust for outliers, as by the method described below for the        linear method.)    -   2. The relationship of Z_(i) to z₂, z₂ to z₃, . . . z_(n−1) to        z_(n) is/are specified parameter(s) defining the shape of the        decline object, these specified parameters can be derived from        the processor based calculations of the dipole slopes made from        a user selected consecutive data set or from a set drawn by the        user onto a scaled grid.

To recognize a rise event a similar method is applied wherein z₁, z₂, .. . , z_(n) are greater than zero. Complex events, which include riseand fall components are built from these more composite objects.Alternatively, a specific magnitude of change along a dipole slopedataset can be used to specify a complex object comprised of twocomposite objects separating at the point of change (a waveformdeflection point). In one application the user slides the cursor overthe portion of the wave, which is to be selected, and this region ishighlighted and enlarged and analyzed with respect to the presence ofmore composite objects. The dimensions of the object and the slope dataset, which defines it, can be displayed next to the enlarged waveform.If the object is complex (as having a plurality of segments of differingslope polarity or having regions wherein the slope rapidly changes as bya selectable threshold), then each composite object is displayedseparately with the respective dimensions and slope data sets. In thisway the operator can confirm that this is the actual configurationdesired and the user is provided with a summary of the spatial anddimensional characteristics of the composite objects, which define theactual selected region. The operator can select a range of variations ofthe slope data set or chance the way in which the composite objects aredefined, as by modifying the threshold for a sustained change in slopevalue along the slope dataset. For example, by allotting at least oneportion of the slopes to vary by a specified amount, such as 10%, byinputting graphically the variations allowed. If the operator “OKs” thisselection, the processor searches the entire timed dataset for thecomposite objects, building the selected object from the compositeobjects if identified.

To recognize a decline event by applying the linear method according tothe present invention disclosure, let {x_(i), x_(i+1), . . . , x_(r)} bea set of consecutive points and let s=(x_(r)−x_(d)/(r−i) be the overallslope of these points. (The slope could be defined by using linearregression, but one definition in accordance with present embodimentsallows for improved fidelity of the output by allotting rejection basedon outlier identification, which will be discussed). Then {x_(i),x_(i+1), . . . x_(r)} is a decline if it satisfies the followingconditions:

-   -   3. x_(i)>x_(i+1)2> . . . x_(r), i.e. the parameter level of the        patient is continually falling over the set of points. (This        condition will be partially relaxed to adjust for outliers, as        described below.)    -   4. r−i≧D_(min), where D_(min) is a specified parameter that        controls the minimum duration of a decline.    -   5. s_(min)≦s≦s_(max), where s_(min) and s_(max) are parameters        that specify the minimum and maximum slope of a decline,        respectively.

The set {97, 95, 94, 96, 92, 91, 90, 88}, does not satisfy the currentdefinition of a decline even though the overall level of the parameteris clearly falling during this interval. The fourth data point, 96, isan outlier to the overall pattern. In order to recognize this intervalas a decline, the first condition must be relaxed to ignore outliers.The modified condition 1 is:

-   -   1*. Condition 1 with Outlier Detection        -   a. x_(i)>x_(i+1),        -   b. x_(i)>x_(i+1) or x_(i+1)>x_(j+2) for j=i+1, . . . , r−2.        -   c. x_(r−1)>x_(r).

To recognize a rise event, let {x_(i), x_(i+1), . . . , x_(r)} be a setof consecutive points and let s=(x_(r)−x_(i)/(r−i) be the overall slopeof these points. Then {x_(i), x_(i+1), . . . , x_(r)} is a rise if itsatisfies the following conditions:

-   -   1. x_(i)<x_(i+1)< . . . <x_(r), i.e., the parameter level of the        patient is continually rising over the set of points. (This        condition will be partially relaxed to adjust for outliers, as        described below.)    -   2. r−i≧D_(min), where D_(min) is a specified parameter that        controls the minimum duration of rise.    -   3. s_(min)≦s≦s_(max), where s_(min) and s_(max) are parameters        that specify the minimum and maximum slope of a decline,        respectively.

Similar to declines, the first condition of the definition of arise isrelaxed in order to ignore outliers. The modified condition 1 is:

-   -   1*. Condition 1 with Outlier Detection    -   a. x_(i)<x_(i+1).    -   b. x_(j)<x_(j+1) or x_(j+1)<x_(j+2) for j=i+1, . . . , r−2.    -   c. X_(r−1)<x_(r).

To recognize a negative pattern, the program iterates through the dataand recognizes events and then identifies event relationships to definethe patterns. The system uses polarities (as defined by the direction ofparameter movement in a positive or negative direction) to test forcondition (1*) rather than testing for greater than or less than. Thissimplifies the computer code by permitting the recognition of alldecline and rise events to be combined in a single routine and ensuresthat decline events and rise events do not overlap, except that they mayshare an endpoint. The tables below show how condition (1*) can beimplemented using polarities.

Equivalent Condition 1* for Decline event Condition 1* EquivalentCondition a. x_(i) > x_(i −1) p_(i) = −1 b. x_(i) > x_(j −1) orx_(j −1) > x_(j −2) p_(i) = −1 or p_(j+1) = −1 c. x_(r−1) > x_(r)p_(r−1) = −1

Equivalent Condition 1* for Rise event Condition 1* Equivalent Conditiona. x_(i) < x_(i 1) p_(i) = 1 b. x_(i) < x_(j−1) or x_(j−1) < x_(j−2) p₁= 1 or p_(j+1) = 1 c. x_(r−1) < x_(r) p_(r−1) = 1

The pseudocode for the combined microprocessor method, which recognizesboth unipolar decline events and unipolar rise events, is shown below.In this code, E is the set of events found by the method, where eachevent is either a decline or a rise.

Event Recognition i = 1 event_polarity = p₁ for j = 2 to n−2   if (p_(i)≠ event_polarity) and (p_(i−1) ≠ event_polarity)    r = j    X = ¦ x_(p.)...x_(r) ¦    if event_polarity = 1       Add X to E if itsatisfies rise conditions (2) and (3)    elseif event_polarity = −1        Add X to E if it satisfies decline conditions         (2) and(3)      endif      i = j      event_polarity = p_(i)     endif   endforAdd X={x_(i), . . . , x_(n)} to E if it satisfies either the rise ordecline conditions.

Next, A specific pattern is recognized by identifying a certain sequenceof consecutive events, as defined above, which comply with specificspatial relationships. For example, a negative pattern is recognizedwhen a decline event, say D={x_(i), . . . , x_(j)}, together with a riseevent, say R={x_(k), . . . , x_(m)}, that closely follows it. Inparticular, D and R must satisfy k−i≦t_(dr), where t_(dr) is aparameter, specified by the user, that controls the maximum amount oftime between D and R to qualify as a negative pattern.

The exemplary pseudocode for the microprocessor system to recognize anegative pattern is shown below. Let Ē={E₁, E₂, . . . , E_(q)} be theset of events (decline events and rise events) found by the eventrecognition method, and let DR be the set of a negative pattern.

Negative Pattern Recognition for h = 1 to q−1   Let D ={x_(i),...,x_(j), } be the event E_(h)   if D is a decline event      Let R = {x_(k),...,x_(m)} be the event E_(h+1)    if R is a riseevent       gap = k − j       if gap ≦ t_(dr)           Add (D,R) to thelist. DR of negative patterns       endif    endif   endif endfor

As noted, a cluster is a set of consecutive negative or positivepatterns that appear close together. In particular, let C={DR_(i),DR_(i+1), . . . , DR_(k)} be a set of consecutive negative patterns,s_(j) be the time at which DR_(j) starts, and e_(j) be the time at whichDR_(j) ends. Then C is a cluster if it satisfies the followingconditions:

-   -   1. s_(j+1)−e_(j)≦t_(c), for j=i, . . . , k−1, where t_(c) is a        parameter, specified by the user, that controls the maximum        amount of time between consecutive negative patterns in a        cluster.    -   2. k−i−1≧c_(min), where e_(min) is a parameter, specified by the        user, that controls the minimum number of negative patterns in a        cluster.

The pseudocode for the algorithm to recognize clusters of negativepatterns is shown below. Let DR={DR₁, DR₂, . . . , DR_(r)} be the set ofnegative patterns found by the above pattern recognition method.

Cluster Recognition (of negative patterns) f = 1;   for h = 2:r     LetR = ¦ x _(l),.....x_(m) ¦ be the rise in DR_(h−1)     Let D = ¦x_(i),.....x_(j) ¦ be the decline in DR_(h)     gap = i − m     if gap >t_(c)         g = h − 1         if g − f + 1 ≧ c_(min)         AddDR_(1·) DR_(i·1)...., DR_(g) to the list of clusters    endif    f = h  endif endfor g = r if g − f − 1 ≧ c_(min)   Add DR_(i − DRi−1).....DR_(g) to the list of clusters endif

According to the present invention, this object-based linear method mapsthe unique events, patterns and clusters associated with airwayinstability because the sequential waveform events associated withairway closure and reopening are each both rapid, substantially unipolarand relatively linear. Also, the patterns and clusters derived arespatially predictable since these precipitous physiologic changes arepredictably subject to rapid reversal by the physiologic control system,which is attempting to maintain tight control of the baseline range.Because timed data sets with predictable sequences of precipitousunidirectional deflections occur across a wide range of parameters, thesame digital pattern recognition methods can be applied across a widerange of clustering outputs, which are derived from airway instability.Indeed the basic underlying mechanism producing each respective clusteris substantially the same (e.g. clusters of positive pulse ratedeflections or positive airflow amplitude deflections). For this reason,this same system and method can be applied to a timed data set of theoxygen saturation, pulse rate (as for example determined by a beat tobeat calculation), amplitude of the deflection of the chest wallimpedance waveform per breath, amplitude of deflection of the airflowsignal per breath (or other correlated of minute ventilation), systolictime intervals, blood pressure, deflection amplitude of the nasalpressure, the maximum exhaled CO2 per breath, and other signals.Additional details of the application of this digital patternrecognition method to identify clusters are provided in patentapplication Ser. No. 09/409,264 to the present inventor.

Next, for the purpose of building the multi-signal object, a pluralityof physiologically linked signals are analyzed for the purpose ofrecognizing corresponding patterns and corresponding physiologicconvergence for the optimal identification of the cluster cycles. Forexample, a primary signal such as airflow is analyzed along with acontemporaneously measured secondary signal such as oxygen saturation asby the method and system discussed previously. As discussed previously,for the purpose of organizing the data set and simplifying the analysis,the raw airflow signal is processed to a composite object level. Forexample, the composite level of airflow can be a data set of theamplitude and/or frequency of the tidal airflow as by thermister orpressure sensor, or another plot, which is indicative of the generalmagnitude of the timed tidal airflow. In the presently preferredembodiment, a mathematical index (such as the product) of the frequencyand amplitude is preferred, because such an index takes into account theimportant attenuation of both amplitude and frequency during obstructivebreathing. Furthermore, both the frequency and amplitude are oftenmarkedly increased during the recovery interval between apneas andhypopneas. It is not necessary that such a plot reflect exactly the truevalue of the minute ventilation but rather, it is important that theplot reflect the degree of change of a given level of minuteventilation. Since these two signals are physiologically linked, anabrupt change in the primary signal (airflow) generally will producereadily identifiable change in the subordinate signal (oxygensaturation). As previously noted, since the events which are associatedwith airway collapse are precipitous, the onset of these precipitousevents represent a brief period of rapid change which allows for optimaldetection of the linkage between the primary signal and the subordinatesignal.

The signals can be time matched by dipole slopes at the fundamentallevel. In addition, in one embodiment, the point of onset of precipitouschange is identified at the composite object level of the primary signaland this is linked to a corresponding point of a precipitous change inthe composite object level of the subordinate signal. This is referredto herein as a delta point. As shown in FIGS. 9, 10, and 11, a firstdelta point is identified in the primary signal and in this example isdefined by the onset of a rise object. A corresponding first delta pointis identified in the subordinate signal and this corresponds to theonset of a rise object in the subordinate signal. A second delta pointis identified which is defined by the point of onset of a fall object inthe primary signal and which corresponds to a second delta point in thesubordinate signal defined by the onset of a fall event in the secondarysignal. The point preceding the second delta point (the“hyperventilation reference point”) is considered a reference indicatingan output associated with a degree of ventilation, which substantiallyexceeds normal ventilation and normally is at least twice normalventilation. When applying airflow as the primary signal and oximetry asthe subordinate signal, the first delta point match is the most precisepoint match along the two integrated waveforms and therefore comprises a(“timing reference point”) for optimally adjusting for any delay betweenthe corresponding objects of the two or more signals. The mathematicalaggregate (such as the mean) of an index of the duration and slope,and/or frequencies of composite rise and fall objects of the fundamentallevel of tidal ventilation along a short region adjacent these referencepoints can be applied as a general reference for comparison to definethe presence of relative levels of ventilation within objects alongother portions of the airflow time-series. Important fundamental objectcharacteristics at these reference points are the slope and duration ofthe rise object or fall object because these are related to volume ofair, which was moved during the tidal breath. The fundamental objectscomprising the tidal breaths at the reference hyperventilation pointalong the composite level are expected to have a high slope (absolutevalue) and a high frequency. In this way, both high and low referenceranges are determined for the signal. In another embodiment, thesepoints can be used to identify the spatial shape configuration of therise and fall objects at the fundamental level during the rise and fallobjects at the composite level.

As shown in FIGS. 9 and 10, using this method at the composite objectlevel, a first object (FIG. 11) can then be identified in the primarysignal between the first delta point and the second delta point which isdesignated a recovery object. As also shown in FIG. 11 the matchedrecovery object is also identified in the subordinate signal as thepoint of onset of the rise object to the point of the onset of the nextsubsequent fall object. In the preferred embodiment, the recovery objectis preceded by the apnea/hypopnea object which is defined by the pointof onset of the fall object to the point of onset of the next riseobject in both the primary and subordinate signals.

As shown in FIG. 12, a recovery object recognized at the composite levelcan be used to specify a region for comparison of sequential objects atthe fundamental object level. Here, upon recognition of the presence ofa recovery object (where it is anticipated that the ventilation effortwill be high) the ratio of the slope of exhalation objects to the slopeof inhalation objects can be compared within the recovery object and thetime-series derived from these comparisons can be plotted if desired.During upper airway obstruction, the inspiration is slowed to a greaterdegree than exhalation. The magnitude change of the ratio during theclusters of apneas provides an index of the magnitude of upper airwaynarrowing (which selectively slows inhalation during the clusteredapnea/hypopnea objects). However, during the recovery object or at the“hyperventilation reference point”, the upper airway should be wide openfor both inhalation and exhalation and this can be used as a referencebecause, during this time, the absolute slope of the fundamental objectsduring recovery can then be compared to the absolute slope of thefundamental objects during other times along the night to provide anindication of upper or looser airway narrowing.

When airflow is the primary signal and oximetry the subordinate, themost reliable delta point is the point of onset of a rapid rise inventilation (in a patient with an oxygen saturation, at the point ofonset point, of less than 96-97%). Patients with very unstable airwayswill generally have relatively short recovery objects. Other patientswith more stable airways may have a multi-phasic slope of decline inairflow during the recovery objects herein, for example, there is aninitial precipitous decline event in the airflow parameter and then aplateau or a much more slight decline which can be followed by a secondprecipitous decline to virtual absence of ventilation. Using the slopedipole method these composite objects can be readily separated such thatthe occurrence of multiple composite objects (especially wherein theslopes are close to zero) or a single object Faith a prolonged slowlyfalling slope dataset occurring immediately after the first data point,can be identified. These patients generally have longer recoveryintervals and more stable airways. The identification of a declineobject associated with decline from the hyperventilation phase ofrecovery followed by a plateau and/or a second decline object associatedwith the onset of apnea is useful to indicate the presence of a greaterdegree of airway stability. Accordingly, with the airflow signal, athird delta point (FIG. 12) designated a “airflow deflection point” canoften be identified in the airflow tracing corresponding to thedeflection point at the nadir of drop in airflow at the end of therecovery. This point is often less definable than the second delta pointand for this reason matching the second delta points in the airflow andoximetry signals is preferred although with some tracings a matchbetween the airflow deflection point and the second delta point in theoximetry dataset provides a better match.

If a significant decline in airflow is identified after the “airflowdeflection point”, then the region of the intervening decline object andthe next delta point (onset of the next recovery) is designated areference “ventilation nadir region”. If the region or object(s) fromthe second delta point to ventilation deflection point is very short (as0-3 breaths) and the ventilation nadir region has a mean slope close toor equal to zero (i.e. the region is relatively flat) and the deflectionamplitude is close to zero or otherwise very small indicating now orvery little ventilation, then the airway is designated as highlyunstable.

Another example of object processing at the fundamental object level, inaccordance with present embodiments, includes the processor-basedidentification of fluttering of the plateau on the pressure signal torecognize partial upper airway obstruction. During the nasal pressuremonitoring a fluttering plateau associated with obstructive breathingoften occurs intervening a rise event and a fall event of tidalbreathing. Since the plateau objects are easily recognizable at thefundamental level and readily separated using the present objectrecognition system the plateau can be processed for the tiny rise andfall objects associated with fluttering and the frequency of theseobjects can be determined. Alternatively, a Fourier transform can beapplied to the plateau objects between the rise and fall events of thenasal pressure signal to recognize the presence of fluttering or anothermethod can be utilized which provides an index of the degree offluttering of the plateau objects.

Since reduced effort also lowers the slope of exhalation andinspiration, the configuration (as defined by the slope dataset of thedipoles defining the fundamental objects of both inspiration andexpiration at the reference objects) can be applied as referencefundamental object configurations defining the presence ofhyperventilation or hypopnea. This process is similar to the selectionprocess for identifying search objects described earlier but in thiscase the input region is pre-selected. In an example, the range ofcharacteristics of the objects at the fundamental level derived from oneor more tidal breaths occurring prior to the second airflow delta pointcan be used to designate a reference hyperventilation objects range.Alternatively, the object based characteristics defined by the range ofcharacteristics of the objects derived from one or more tidal breathsoccurring prior to the first airflow delta point can be used todesignate a reference hypopnea object's range. The processor can thenautomatically assess object ranges along other points of the tracing. Inthis way the processor can apply an artificial intelligence process tothe identification of hypopneas by the following process:

-   -   1. Identify the region wherein a hypopnea is expected (as for        example two to three tidal breaths prior to the first airflow        delta point).    -   2. Select this as a region for objects processing to define the        characteristics of hypopneas in this patient.    -   3. Process the region using the slope dipole method to define        the range of fundamental objects comprising the target region.    -   4. Compare the identified range of objects to other analogous        objects along to tracing to identify new objects having similar        characteristics.    -   5. Using the criteria derived from the objects defining the        target region search the processed waveform for other regions        having matching sequences of new objects and identify those        regions.    -   6. Provide an output based on said identification and/or take        action (e.g. increase CPAP) based on said identification.

These processing methods exploit the recognition that certain regionsalong a multi-signal object (as within a cluster) have a very highprobability of association with certain levels of ventilation. Theobjects defining those regions can then be used as a reference or as anopportunity to examine for the effects of a given level of ventilationeffort on the flow characteristics. Patients with obstructive sleepapnea will have a fall in the slopes of fundamental inspiration objectsduring decline objects at the composite level indicative of upper airwayocclusion. Also, as shown in FIG. 12, patients with asthma or chronicobstructive lung disease will have a reduced slope of the exhalationwhen compared to the slope of inhalation during the rise objects betweenapneas at the base level. According to one embodiment of the presentdisclosure, the time-series of the ratio of the slope of inhalationobjects to exhalation objects is included with the basic time-series.Patients with simple, uncomplicated obstructive apnea will have clustersof increasing slope ratios with the ratio rising to about one during therecovery objects. Patients with combined obstructive apnea and asthma orchronic obstructive lung disease will have a greater rise in sloperatios during the recovery objects to into the range of 2-3 or greater,indicating the development of obstructive lower airways during the rapidbreathing associated with recovery.

One system for processing, analyzing and acting on a time-series ofmulti-signal objects in accordance with present embodiments is shown inFIG. 8. The examples provided herein show the application of this systemfor real time detection, monitoring, and treatment of upper airway andventilation instability and for the timely identification ofpathophysiologic divergence. The system includes a portable bedsideprocessor 10 having at least a first sensor 20 and a second sensor 25,which may provide input for at least two of the signals discussed supra.The system includes a transmitter 35 to a central processing unit 37.The bedside processor 10 includes an output screen 38, which providesthe nurse with a bedside indication of the sensor output. The bedsideprocessors can be connected to a controller of a treatment orstimulation device 50 (which can include a positive pressure deliverydevice, an automatic defibrillator, a vibrator or other tactilestimulator, or a drug delivery system such as a syringe pump or back tothe processor to adjust the analysis of the time-series inputs), thecentral unit 37 preferably includes as output screen 55 and printer 60for generating a hard copy for physician interpretation. According topresent embodiments, as will be discussed in detail, the system therebyallows recognition of airway instability, complications related to suchinstability, and pathophysiologic divergence in real time from a singleor multiple inputs. The bedside processor is connected to a secondaryprocessor 40 which can be a unit, which performs measurementsintermittently and/or on demand such as a non-invasive blood pressuremonitor or an ex-vivo monitor, which draws blood into contact with asensor on demand for testing to derive data points for addition to themulti-signal objects. The secondary monitor 40 includes at least onesensor 45. The output of the bedside processor can either be transmittedto the central processor 37 or to the bedside monitor 10 to render a newobject output, action, or analysis.

The method of hypopnea recognition discussed previously can be coupledwith a conventional CPAP auto titration system which can comprise onetreatment device of FIG. 8 to improve CPAP titration. The previouslydescribed method for detecting hypopneas is particularly useful toidentify milder events because, while the configuration of each tidalbreath of within the hypopnea may be only mildly different, there is acumulative decline in ventilation or increase in airway resistance whichoften, eventually directly triggers a recovery object or triggers anarousal which then triggers the occurrence of a recovery object. Therecovery objects being a precipitous response to a mild but cumulativedecline on airflow is easier to recognize and is exploited to specifytiming of the target processing as noted above.

One of the problems with conventional CPAP is that many of them (if notall) operate with pre-selected criteria for recognition of a hypopnea(such as 50% attenuation of a breath or group of breaths when comparedwith a certain number of preceding breaths.). These systems generallydetermine the correct pressures for a given patient by measuringparameters derived from the algorithms which monitor parameters throughthe nasal passage. Unfortunately, the nasal passage resistance is highlyvariable from patient to patient and may be variable in a single patientfrom night to night. These simplistic single parameter systems are evenless system is less suitable in the hospital where many confoundingfactors (such as sedation, etc.) may severely affect the performance ofconventional auto titration system. Since most auto-titration systemmonitors their effectiveness through nasal signals their algorithms arelimited by this wide variability of nasal resistance from patient topatient. Studies have shown that, while apneas can be detected, thedetection of hypopneas by these devices is often poor. This becomes evenmore important for the detection of mild hypopneas, which can be verydifficult to reliably detect (without an unacceptably high falsepositive rate) through a nasal signal alone. Indeed these milderhypopneas are more difficult characterize and not readily definable as aset of function of a set of predetermined rules for general applicationto all patients.

One process of applying the system of FIG. 8 to customize hypopnearecognition to match a given patients nasal output is represented inFIG. 17. The present disclosure includes an auto titration system, whichadjusts its titration algorithm (which can be any of the conventionalalgorithms) based on the configurations of the multi-signal object,which can include oximetry, chest wall movement, or EEG data sets. Withthis system, for example, the initial titration algorithm is appliedwith the data set of CPAP pressure becoming part of the multi-signalobject. The object time-series at the composite level is monitored forthe presence of persistent clusters (especially clustered recoveryobjects or clustered EEG arousals). If these are identified then theregion of the cluster occurrences is compared to the identified hypopnearegion derived from the conventional method. If this region is asrecognized as hypopneas then the pre-selected pressure for a givenincrement in titration is further incremented by 1-2 cm so thatconventional titration occurs at higher-pressure levels and the processis repeated until all clusters are eliminated. (If EEG arousals worsenwith this increase then the increment can be withdrawn). If on the otherhand the algorithm did not recognize this region as a hypopnea thethreshold criteria for a hypopnea is reduced until the clusters areeliminated (in some cases require a baseline fixed pressure of 2-3 ormore cm.). FIG. 17 shows a CPAII auto-titration system which uses themulti-signal object dataset during one or more auto adjusting learningnights to customizes at least one of the treatment response to a giventriggering threshold or the triggering threshold to a given treatmentresponse. The application of a learning night can prevent inappropriateor unnecessary adjustments and can provide important information abouttreatment response while assuring that the basic algorithm itself iscustomized to the specific patient upon which it is applied. This isparticularly useful in the hospital using hospital-based monitors wherethe monitor is coupled with the processor of the CPAP unit for thelearning nights while in the hospital. In one embodiment, learningnights can be provided at home by connecting a primary processor forprocessing multiple signals with the processor of the CPAP unit for afew nights to optimize the algorithm for later use. In the hospital allof the components can be used to assure optimal titration, using the anobjects based cluster analysis of simultaneous tracing of chest wallimpedance and oximetry the titration can be adjusted to assuremitigation of all clusters, alternatively, if they are not mitigated bythe titration then the nurse is warned that these clusters arerefractory and to consider central apnea (particularly if the impedancemovements during the apneas are equivocal or low). If for example, thepatient's oxygen saturation falls (after adjusting for the delay) inresponse to an increase in pressure, the pressure can be withdrawn andthe nurse warned that desaturation unresponsive to auto titration isoccurring or bilevel ventilation can be automatically initiated. Theself-customizing auto titration system can include a pressure deliveryunit capable of auto adjusting either CPAP or BIPAP such that such adesaturation in response to CPAP can trigger the automatic applicationof BIPAP.

As discussed, according to the present disclosure, clusters of hypopneascan generally be reliably recognized utilizing with only a singleparameter. However, when significant signal noise or reduced gain ispresent, the objects based system can combine matched clusters within atime-series of multi-signal objects in the presence of sub optimalsignals by providing a scoring system for sequential objects. FIGS. 13,14, and 15 show schematics of the basic cluster matching in situationswherein sub optimal signals may be present. The multi-signal objectsdefining the matched clusters of paired timed datasets of airflow andoximetry include a matched sequence of negative cycle objects in theairflow signal and corresponding negative cycle object in the oximetrysignal. Each cycle object is defined by a set of coupled rise and fallobjects meeting criteria and occurring within a predetermined intervalof each other (as discussed previously). The occurrence of a cycleobject in either dataset meeting all criteria is given a score of 1. Thecycles are counted in sequence for each multi-signal cluster object. Forthe purpose of illustration, according to the present disclosure, theoccurrence of a score of 3 in any one signal (meaning that a sequence of3 cycles meeting criteria have occurred within a specified interval)provides sufficient evidence to identify a cluster object. When twosimultaneous signals are processed, a total score of 4, derived fromadding the number of cycles meeting criteria in each signal, issufficient to indicate the presence of a cluster object. In this mannerthe cluster is continued by a sequential unbroken count greater than 3with one signal, or greater than 4 with two signals. Once the presenceof a cluster object has been established along the time-series, at anypoint along the cluster object the sequential count along one signal canbe converted to a continuation of the sequential count along anothersignal allowing the cluster object to continue unbroken. The failure ofthe occurrence of a cycle meeting criteria within either signal within aspecified interval (for example about 90-120 seconds, although otherintervals may be used) breaks the cluster object. A new cluster objectis again identified if the count again reaches the thresholds as notedabove. It can be seen that this scoring method takes into account thefact that artifact often affects one signal and not another. Thereforeif either signal alone provides a sufficient score, the presence of acluster object is established. In addition, the effect of brief episodesof artifact affecting both signals is reduced by this scoring method. Inthis way, artifact, unless prolonged, may cause the cluster object to bebroken but as soon as the artifact has reduced sufficiently in any oneor more signals the process of scoring for a new cluster object willrestart.

Another CPAP auto titration system according to the present disclosureincludes a processor and at least one sensor for sensing a signaltransmitted through the nose such as a pressure signal indicative ofairflow, sound and/or impedance as is known in the art. An oximeter,which can be detachable or integrated into the CPAP unit, is connectedwith the processor. The processor detects hypoventilation using outputfrom both the flow sensor and the oximeter, when the oximeter isattached, and in the embodiment with a detachable oximeter, when theoximeter is not attached the processor detects hypoventilation using theflow sensor without oximetry.

According to another aspect of the present disclosure, the multi-signalobject time-series can be used for identifying pathophysiologicdivergence. Pathophysiologic divergence can be defined at thefundamental, composite, or complex level. An example of divergence atthe fundamental level is provided by the relationship between an airflowrise object (inspiration) and a fall object (expiration). Along atime-series of matched expiration and inspiration objects, theoccurrence of a marked increase in amplitude of inspiration is commonlyassociated with an increase in the ratio of the absolute value ofinspiration slope to the absolute value of the slope of exhalation.Should this value increase, this provides evidence suggestingpathophysiologic divergence. Alternatively, in one embodiment, theevaluation time period can be much longer. In one embodiment, theobjects defining the data set of the first time interval are compared tothe objects defining the data set of the second corresponding timeinterval. This comparison is performed in a similar manner to theaforementioned comparison of corresponding cluster objects noted supra.The specific parameters, which are compared, are parameters having knownpredictable physiologic linkages wherein a change of first physiologicparameter is known to induce a relatively predictable change in a secondphysiologic parameter. The second parameter is, therefore, aphysiologically subordinate of the first parameter. As shown in FIG. 11,the first parameter can be a measure indicative of the timed volume ofventilation and the second parameter can be the timed arterial oxygensaturation. Here, as shown in FIG. 11, a progressive rise in minuteventilation is expected to produce a rise in oxygen saturation. Thealveolar gas equation, the volume of dead space ventilation and theoxyhemoglobin disassociation curve predict the rise in oxygen saturationby known equations. However, according to one aspect of the presentdisclosure, it is not necessary to know the absolute predicted value ofoxygen saturation rise for a given change in minute ventilation butrather the processor identifies and provides an output indicatingwhether or not an expected direction of change in the subordinate oneparameter occurs in association with a given direction of change in theprimary parameter. For example, with respect to arterial oxygensaturation and ventilation, it is the preferred purpose of oneembodiment of the present disclosure to determine whether or not anexpected direction and/or slope of change of oxygen saturation occur inassociation with a given direction and/or slope change in minuteventilation. The time course of the rise in ventilation of FIG. 11 isshort however, as the time period lengthens the relationship isstrengthened by the greater number of corresponding measurements and thegreater measurement time. When minute ventilation slopes or trendsupward over a sustained period, after the anticipated delay there wouldbe an expected moderate upward change in oxygen saturation if thesaturation is not already in the high range of 97-100%. On the otherhand, if the oxygen saturation is falling during this period, this wouldsuggest that the patient is experiencing a divergent pathophysiologicresponse which may warrant further investigation. Automatic recognitionof falling or unchanged oxygen saturation in association with a risingminute ventilation can provide earlier warning of disease than isprovided by the simple non-integrated monitoring and analysis of thesetwo wave forms.

One of the advantages provided by the present disclosure is that it isnot necessary to be exact with respect to the measurement of minuteventilation. Minute ventilation can be trended by conventional methods,without an absolute determination of the liters per minute for example,by plotting a measure of the amplitude and frequency of a nasal oralthermister or by the application of impedance electrodes on the chest,thereby monitoring the amplitude and frequency of tidal chest movement.Alternatively, conventional impedance or stretch sensitive belts aroundthe chest and abdomen or other measures of chest stall and/or abdominalmovement can be used to monitor tidal ventilation and then this can bemultiplied times the tidal rate of breathing to provide a general indexof the magnitude of the minute ventilation. In one embodiment, theminute ventilation are trended on a time data set over a five to thirtyminute intervals along with the oxygen saturation.

In one embodiment, the monitoring system for identification ofpathophysiologic divergence of timed output is shown in FIG. 8. Asdiscussed previously, the monitor includes a microprocessor 10, thefirst sensor 20, a second sensor 25, and an output device 38 which canbe a display or a printer, but preferably would include both. Theprocessor 10 is programmed to generate a first timed waveform of thefirst parameter, derived from the first sensor 20, and a second timedwaveform of second parameter, derived from the second sensor 25. Usingthe multi-signal processing system, described previously, the processor10 compares the objects of the first timed output to the objects of thesecond timed output to identify unexpected divergence of the shape ofthe first timed output to the shape of the second timed output andparticularly to recognize a divergence in directional relationship orpolarity of one timed output of one parameter in relationship to anothertimed output of another related parameter. In one embodiment, thisdivergence comprises a fall in the slope of the oxygen saturation (forexample, as defined by the recognition of a “decline object”, asdiscussed previously) in relationship to a rise (referred to as a “riseobject”) in the slope of the corresponding minute ventilation. Inanother example, the processor integrates three signals to identifydivergence. The processor identifies the relationship of other signalssuch as heart rate or R-to-R interval or a measure of the pulsemagnitude (as the amplitude, slope of the upstroke, or area under thecurve of the plethesmographic pulse). In particular, a rise object inminute ventilation may be identified in association with a declineobject in oxygen saturation and a decline object in heart rate or pulseamplitude. These outputs can be plotted on a display 30 for furtherinterpretation by a physician with the point of pathophysiologicdivergence of one parameter in relationship to another parameteridentified by a textural or other marker.

The identification of pathophysiologic divergence can result insignificant false alarms if applied to the short time intervals used forrise and decline objects which are used for detection of cluster objects(and also the short averaging intervals for this purpose). Inparticular, if the identification of divergence is applied for shortintervals, such as 1 to 2 minutes, a significant number of falseepisodes of divergence will be identified. One purpose of the presentinvention is to provide clear evidence of a trend in one measuredparameter in relationship to a trend of another measured parameter sothat the strong definitive evidence that divergence has indeed occurred.According to the present disclosure, this can be enhanced by theevaluation of the prolonged general shape or polarity of the signal sothat it is considered preferable to identify divergence over segments offive to thirty minutes. The averaging of many composite objects toidentify a rise object at the complex object level helps mitigate suchfalse alarms. For this reason, the expected time course of a divergencetype must be matched with the resolution (or averaging times) of theobjects compared.

According to one aspect of the disclosure, to enhance the reliability ofthe analysis of the timed data set, the averaging interval, for thispurpose, can be adjusted to avoid excessive triggering of theintermittent monitoring device. In one embodiment, the averaginginterval is increased to between thirty and ninety seconds or only theanalysis of complex objects can be specified. Alternative methods may beused to identify a rise and fall objects such as the application of lineof best-fit formulas, as previously discussed. Elimination of outlierdata points to define larger composite objects can also be applied asalso previously discussed or by other methods. In this way theidentification of a trend change, which evolves over a period of five tofifteen minutes, can be readily identified. The identification ofdivergence can produce a textual output, which can be maintained for afinite period until the secondary parameter corrects or a thresholdperiod of time has elapsed. For example, if a rise in minute ventilationis identified over a predetermined interval period (such as about tenminutes) to define a rise object and a fall in oxygen saturation isidentified over a corresponding period to define a fall object, theprocessor identifies the presence of divergence and can produce atextual output which can be provided on the bedside display or centralprocessing display. This textual output can be maintained for a finiteperiod, for example, one to two hours, unless the oxygen saturationreturns to near its previous value, at which time the textual output maybe withdrawn from the display.

In this way the presence of pathophysiologic divergence is readilyidentified, however, since divergence is defined by divergent rise andfall objects of corresponding physiologically linked parameters, itsduration is necessarily limited since these slopes can not continue todiverge indefinitely. It is important to carry forward theidentification of prior divergence in the patient's display for at leasta limited period of time so that the nurse can be aware that this eventhas occurred. For example, a “fall object” identified in the secondarysignal such as a fall in oxygen saturation from 95% to 90% over a periodof ten minutes occurring in association with a rise object in theprimary signal, such as, for example, a doubling of the amplitude of theairflow or chest wall impedance deflection over a period often minutescan produce an identification of pathophysiologic divergence which canbe linked to the outputted saturation so that the display shows asaturation of 90% providing an associated textual statement“divergence-TIME”. This identification of divergence can, over a periodof time, be withdrawn from the display or it can be immediatelywithdrawn if the oxygen saturation corrects back close to 95%.

FIG. 7 depicts a schematic of a processing system for outputting and/ortaking action based on analysis of time-series processing in accordancewith certain embodiments of the present technique.

The “Time-Series Analysis Process” block of FIG. 7 may represent asystem component related to analyzing one or more time-series. Inaccordance with present embodiments, time-series analysis can beutilized to analyze multiple time-series of parameters generated by apatient in the assessment of disease. For example, a time-series of apatient's heart rate data may be analyzed. Other examples of parametersthat may be analyzed include oxygen saturation, chest wall impedance,pulse rate, and blood pressure.

The system of FIG. 7 also includes a block titled “Cluster or DivergenceRecognized,” which may represent a system component configured torecognize a cluster or divergence of one or more time-series.Recognition of a cluster may be achieved by analyzing spatial and/ortemporal relationships between different portions of a waveform. Forexample, a cluster may contain a high count of apneas with specifiedidentifying features or patterns that occur within a short time intervalalong the waveform (such as 3 or more apneas within about 5-10 minutes).With regard to divergence, in accordance with present embodiments, achange in configuration of a multi-signal time-series can be used totrigger addition of one or more signals to the multi-signal time-seriesto identify whether or not physiological divergence is occurring withrespect to the new, less frequently sampled signal.

By way of example, a processor in accordance with present embodimentsmay identify a significant rise in heart rate (e.g., a 25% rise and atleast 15 beats per minute) over a period of 5 to 20 minutes. In view ofsuch a rise, a monitor may automatically cause a measurement of bloodpressure to be immediately taken. The processor may compare an output ofthe monitor to a previously recorded value and, if a significant fall inblood pressure (such as a fall in systolic of 15% and more) isidentified in association with the identified rise in heart rate thattriggered the test, a textual warning may be provided indicating thatthe patient is experiencing pathophysiologic divergence with respect toheart rate and blood pressure. The “Output Text Indication” block inFIG. 7 may represent a system component for providing such a warning.

In addition, the system may include features configured to index theseverity of a recognized cluster or divergence, and make a severity tothreshold determination. Such system components may be represented bythe “Severity Indexing” and the “Severity to Threshold” blocksillustrated in FIG. 7. In accordance with present embodiments, theseverity to threshold determination may result in an alarm. For example,mild clustering may result in outputting a single bar on a barindicator, while a more severe clustering may result in generation of alarger warning. Such warnings may be represented by the “Output Alarm”box in FIG. 7.

The severity to threshold determination may also lead to adjusting atreatment, as indicated by the “Adjust Treatment” block. Further, thedetermination may also lead to initiation of a secondary intermittenttest, as shown by the “Initiate Secondary Intermittent Test” block. Theresult of the secondary test may be compared to the prior results, asrepresented by the “Compare with Prior Result” block, which may alsolead to a treatment adjustment. In addition, the results of thecomparison may be combined into a signal integrated output, asillustrated by the “Signal Integrated Output” block, which may activatean alarm.

As discussed previously and as also illustrated in FIG. 8, in anotherembodiment of the present disclosure, a change in the configuration ofthe multi-signal time-series can be used to trigger the addition of oneor more additional signals to the multi-signal time-series, such as anon-invasive blood pressure, to identify whether or not pathophysiologicdivergence is occurring with respect to the new, less frequently sampledsignal. For example, the trending rise in heart rate should not begenerally associated with a fall in blood pressure. If, for example overa period of 5 to 20 minutes, a significant rise in heart rate (as forexample a 25% rise and at least 15 beats per minute) is identified bythe processor, according to the present disclosure, the monitor canautomatically trigger the controller of a non-invasive blood pressuremonitor to cause the measurement of blood pressure to be immediatelytaken. The output of the non-invasive blood pressure monitor is thencompared by the processor to the previous value which was recorded fromthe blood pressure monitor and, if a significant fall in blood pressure(such as a fall in systolic of 15% and more) is identified inassociation with the identified rise in heart rate which triggered thetest, a textual warning can be provided indicating that the patient isexperiencing pathophysiologic divergence with respect to heart rate andblood pressure so that early action can be taken before either of thesevalues reach life-threatening levels. According to another aspect of thedisclosure, a timed dataset of the pulse rate is analyzed, if asignificant change (for example a 30-50% increase in the rate or a30-50% decrease in the interval or a 50-75% increase in the variabilityof the rate), then the blood pressure monitor can be triggered todetermine if a significant change in blood pressure has occurred inrelation to the change in pulse rate or the R to R interval.

This can be threshold adjusted. For instance, a significant rise inheart rate of 50% if lasting for a period of two and a half minutes canbe used to trigger the intermittent monitor, whereas a more modest risein heart rate of, for example, 25% may require a period of five or moreminutes before the intermittent monitor is triggered.

In another embodiment, also represented in FIG. 8, identification by thebedside processor 10 of a sustained fall in oxygen saturation can beused to trigger an ex-vivo monitor 40 to automatically measure thearterial blood gas parameters. Alternatively, a significant rise inrespiratory rate (for example, a 100% increase in respiratory rate forfive minutes) can suffice as a trigger to automatically evaluate eitherthe blood pressure or an ex-vivo monitor of arterial blood gasses.

There are vulnerabilities of certain qualitative indexes of minuteventilation in relationship to divergence, which the present disclosureserves to overcome to enhance the clinical applicability of the output.For example, a rise in the signal from chest wall impedance can beassociated with a change in body position. Furthermore, a change in bodyposition could result in a fall of oxygen saturation due to alterationin the level of ventilation, particularly in obese patients, suchalterations can be associated with an alteration in the ventilationperfusion matching in patients with regional lung disease. Therefore, achange in body position could produce a false physiologic divergence ofthe signals when the multi-signal time-series includes chest wallimpedance and oximetry. For this reason, according to the presentdisclosure, additional time-series components may be required, such asoutputted by a position sensor, or alternatively, if this information isnot available, a more significant fall in one parameter may be requiredin association with a more significant divergent rise in another. Forexample, a significant fall in oxygen saturation of, for example, 4-5%in association with a doubling of the product of the amplitude andfrequency of the impedance monitor would provide strong evidence thatthis patient is experiencing significant pathophysiologic divergence andwould be an indication for a textual output indicating thatpathophysiologic divergence has occurred. The thresholds for definingdivergence, according to the present disclosure, may be selectable bythe physician or nurse. When the time-series output of a positionmonitor is incorporated into the system with a significant change in oneor more parameter, which is temporarily related to a position change, itprovides important additional information.

According to the present disclosure, the magnitude of pathophysiologicdivergence can be provided on the central display 55 or bedside display38. In some cases, as discussed previously, a mild degree ofpathophysiologic divergence may not represent a significant change andthe nurse may want to see, rather, an index of the degree ofpathophysiologic divergence. A bar graph or other variable indicator,which can be readily observed such as the monitoring cubes of FIGS. 6a-6 e, can provide this. In one embodiment the monitoring cube can beselectively time lapsed to observe the previous relational changesbetween parameters, or alternatively the animated object can be rotatedand scaled to visually enhance the represented timed relationships andpoints of divergence.

In one embodiment, the multi-signal time-series output is placed into aformat particularly useful for reviewing events preceding an arrest orfor physician or nurse education. In this format the output controls ananimation of multiple objects which, instead of being parts of a hexagonor cube are shaped into an animated schematic of the physiologic systembeing monitored. The animation moves over time and in response to thesignals and in one embodiment the type of signals (or the reliability ofsuch signals) determines which components of the schematic are “turnedon” and visible. One example includes a multi-signal object defined byoutputs of airflow, thoracic impedance, oximetry, and blood pressurerendering set of a connected set animation objects for the lungs, upperairway, lower airway, heart, and blood vessels which can be animated as;

-   -   Each inspiration causing an animated enlargement of the lungs        tracking the inspiration slope,    -   Each expiration causing an animated reduction in size of the        lungs tracking the expiration slope,    -   Each animated systolic beat of the heart tracks the QRS or        upstroke of the oximetry output,    -   The color of the blood in the arteries and left heart tracks the        oxygen saturation,    -   The diameter of the lower airway (a narrowing diameter can be        highlighted in red) tracks the determination of obstruction by        the slope ratio in situations of hyperventilation (as discussed        previously),    -   The patency of the upper airway (a narrowing or closure can be        highlighted in red) tracks the determination of upper airway        obstruction (as discussed previously).    -   The magnitude of an animated pressure gauge tracks the blood        pressure.

This provides “physiologic animation” which can be monitored inreal-time but will generally be derived and reviewed from the storedmulti-signal objects at variable time scales. This is another example ofan embodiment of the present disclosure providing a quickly, easilyunderstood and dynamic animated output of a highly complex, interactivetime-series derived form a patient. The animation can be reviewed at anincreased time lapsed to speed through evolution of a given patientsoutputs or can be slowed or stopped to see the actual global physiologicstate at the point of arrhythmia onset.

In another example of a more simple signal relationship indicator, apatient with a drop in oxygen saturation of 4% and a doubling of theproduct of the frequency and amplitude of the chest wall impedance tidalvariation may have a single bar presented on the monitor, whereas, apatient with a 6% drop wherein the product of the impedance amplitudeand frequency has tripled may have a double bar, and so on. This allowsreduction in the occurrence of false alarms by providing a bar indicatorof the degree of divergence which has occurred. A similar indicator canbe provided for clustering, indicative of the severity of airway orventilation instability. Since very mild clustering may simply representthe effect of moderate sedation, and not, therefore, represent a causefor great concern (although it is important to recognize that it ispresent). Such a clustering could be identified with a single bar,whereas more severe clustering would generate a larger warning and, itvery severe, an auditory alarm. When the clustering becomes more severeand demonstrates greater levels of desaturation and/or shorter recoveryintervals the bar can be doubled.

In another embodiment, which is particularly useful for neonates, thetime-series of multi-signal objects is derived entirely from a pulseoximeter. Each object level for each signal and further a multi-signalobject time-series of the oxygen saturation and pulse (as for examplecan be calculated below) is derived. This particular multi-signaltime-series has specific utility for severity indexing of apnea ofprematurity. The reason for this is that the diving reflex in neonatesand infants is very strong and causes significant, cumulativebradycardia having a progressive down slope upon the cessation. Inaddition, the apnea is associated with significant hypoxemia, which alsocauses a rapid down slope due to low oxygen storage of these tinyinfants. Even a few seconds of prolongation of apnea causes profoundbradycardia because the fall in heart rate like that of the oxygensaturation does not have a reliable or nadir but rather falls throughoutthe apnea. These episodes of bradycardia cluster in a manner almostidentical to that of the oxygen saturation, the pulse in the neonatebeing a direct subordinate to respiration.

In neonates, oxygen delivery to the brain is dependent both upon thearterial oxygen saturation and the cardiac output. Since bradycardia isassociated with a significant fall in cardiac output, oxygen delivery tothe neonatal brain is reduced both by the bradycardia and the fall inoxygen saturation. It is critical to have time-series measure, whichrelates to cumulative oxygen delivery (or the deficit thereof) both as afunction of pulse and oxygen saturation. Although many indices can bederived within the scope of the present disclosure, the presentlypreferred index is given as the “Saturation Pulse”. Although manycalculations of this index are possible, in one embodiment, the index iscalculated as:

SP=R(SO2-25)

Where:

SP is the saturation pulse in “% beats/sec.”.

R is the instantaneous heart rate in beats per second, and

SO2 is the oxygen saturation of arterial blood in %.

The saturation-pulse is directly related to the brain oxygen delivery.The SPO2-25 is chosen because 25% approaches the limit of extractableoxygen in the neonatal brain. The index is preferably counted for eachconsecutive acquisition of saturation and pulse to produce a continuoustime-series (which is an integral part of a multi-signal time-series ofoxygen saturation and pulse). This index can be calculated for over thetime interval of each apnea and each cluster to derive an apnea orcluster index of saturation-pulse during apnea and recovery in a manneranalogous to that described in U.S. Pat. No. 6,223,064. This provides anenhanced tool for severity indexing of apnea of prematurity in infants.Both the duration and the absolute value of any decrement insaturation-pulse are relevant. If preferred the average maximuminstantaneous, and cumulative deficit of the pulse saturation index canbe calculated for each cluster (as by comparing to predicted normal orautomatically calculated, non apnea related baseline values for a givenpatient).

In this way, according to the present disclosure, a general estimate ofoxygen delivery over time to the infants brain is provided using anon-invasive pulse oximeter through the calculation of both oxygensaturation and pulse over an extended time-series deriving a cumulativedeficit specifically within clusters of apneas to determine index of thetotal extent of global decrease in oxygen delivery to the brain duringapnea clusters. The deficit can be calculated in relation to either thebaseline saturation and pulse rate or predicted normals.

The processor can provide an output indicative of the pulse saturationindex, which can include an alarm, or the processor can trigger anautomatic stimulation mechanism to the neonate, which will arouse theneonate thereby aborting the apnea cluster. Stimulation can include atactile stimulator such as a vibratory stimulator or other device, whichpreferably provides painless stimulation to the infant, thereby causingthe infant to arouse and abort the apnea cluster.

In another embodiment of the system, the recognition of a particularconfiguration and/or order of objects can trigger the collection ofadditional data points of another parameter so that these new datapoints can be added to and compared with the original time-series torecognize or confirm an evolving pathophysiologic process. Oneapplication of this type of system is shown in FIG. 8 and illustratedfurther in FIG. 18. The time-series of pulse, oxygen saturation, and/orcardiac rhythm can be used to trigger an automatic evaluation of bloodpressure by a non-invasive blood pressure device. The bedside processor10, upon recognition of tachycardia by evaluation of the pulse or EKGtracing, automatically causes the controller of the secondary monitoringdevice 40 to initiate testing. The nurse is then immediately notifiednot only of the occurrence, but also is automatically provided with anindication of the hemodynamic significance of this arrhythmia. In thissituation, for example, the occurrence of an arrhythmia lasting for atleast twenty seconds can trigger the automatic comparison of the mostrecent blood pressure antecedent the arrhythmia and the subsequent bloodpressure, which occurred after the initiation of the arrhythmia. Theprocessor identifies the time of the initial blood pressure, whichoccurred prior to the point of onset of the arrhythmia, and the time ofevaluation of the blood pressure after the onset of the arrhythmia andthese are all provided in a textural output so that the nurse canimmediately recognize the hemodynamic significance of the arrhythmia.Upon the development of a pulse less arrhythmia a printed output istriggered which provides a summary of the parameter values over a range(such as the 5-20 minutes) prior to the event as well as at the momentof the event. These are provided in a graphical format to be immediatelyavailable to the nurse and physician at the bedside during theresuscitation efforts so that the physician is immediately aware ifhyperventilation, or oxygen desaturation preceded the arrhythmia (whichcan mean that alternative therapy is indicated).

According to another aspect of the disclosure, if the patient does nothave a non-invasive blood pressure cuff monitor attached, but rather hasonly a pulse oximeter or an impedance based non-invasive cardiac outputmonitor and an electrocardiogram attached, then the multi-leveltime-series plethsmographic pulse objects can be used to help determinethe hemodynamic significance of a given change in heart rate or thedevelopment of an arrhythmia. In this manner, the identification ofsignificant change in the area under the curve associated with asignificant rise in heart rate or the development of an arrhythmia cancomprises a multi-signal object indicative of potential hemodynamicinstability.

If the multi-signal object includes a new time-series of wide QRScomplexes of this occurrence is compared to the area under theplethesmographic pulse to determine the presence of “pulseless” or “nearpulseless” tachycardia. It is critical to identify early pulselesstachycardia (particularly ventricular tachycardia) since cardioversionof pulseless tachycardia may be more effective than the cardioversion ofventricular fibrillation. On the other hand, ventricular tachycardiaassociated with an effective pulse, in some situations, may not requirecardioversion and may be treated medically. Timing in both situations iscritical since myocardial lactic acidosis and irreversible intracellularchanges rapidly develop and this reduces effective cardioversion. It is,therefore, very important to immediately recognize whether or not thesignificant precipitous increase in heart rate is associated with aneffective pulse. The plethesmographic tracing of the oximeter canprovide indication of the presence or absence of an effective pulse.However, displacement of the oximeter from the proper position on thedigit can also result in loss of the plethesmographic tracing. For thisreason, according to the present disclosure, the exact time in which thewide QRS complex time-series developed is identified and related to thetime of the loss of the plethesmographic pulse. If the plethesmographicpulse is lost immediately upon occurrence of a sudden increase of heartrate (provided that the signal does not indicate displacement), this isnearly definitive evidence that this is a pulseless rhythm and requirescardioversion. The oxygen saturation and thoracic impedance portion ofthe multi-signal object is also considered relevant for theidentification of the cause of arrhythmia. At this moment an automaticexternal cardioversion device can be triggered to convert the pulselessrhythm. In an alternative embodiment, as also shown in FIG. 18, a bloodpressure monitor which can be a non-invasive blood pressure monitorintegrated with the automatic defibrillator can be provided. Upon therecognition of a precipitous increase in heart rate, this event cantrigger automatic non-invasive blood pressure evaluation. If thenon-invasive blood pressure evaluation identifies the absence ofsignificant blood pressure and pulse and this is confirmed by theabsence of a plethesmographic pulse, then the processor can signal thecontroller of the automatic cardio version unit to apply and electricalshock to the patient based on these findings. It can be seen thatmultiple levels of discretionary analysis can be applied. The firstbeing the identification of a precipitous development of a wide complextachyarrhythmia in association with simultaneous loss ofplethesmographic pulse which can trigger an automatic synchronizedexternal cardio version before the patient develops ventricularfibrillation. The second requires confirmation by another secondarymeasurement such as loss of blood pressure, the lack of the anticipatedcycle of chest impedance variation associated with normal cardiac outputas with a continuous cardiac output monitor, or other indication.

It can be seen that even without the EKG time-series component object,an analysis of the multi-signal can be applied to compare the area underthe curve of the plethesmographic pulse tracing generated by a pulseoximeter to a plot of peak-to-peak interval of the pulse tracings. Thesudden decrease in the peak-to-peak interval or increase in pulse ratein association with a sudden decrease in the plethesmographic area isstrong evidence that the patient has experienced a hemodynamicallysignificant cardiac arrhythmia. In the alternative, a moderate andslowly trending upward increase in heart rate in association with amoderate and slowly trending downward plot of the area of theplethesmographic pulse would be consistent with intervascular volumedepletion, or ineffective cardiac output resulting from significantsympathetic stimulation which is reducing the perfusion of theextremities as with as congestive heart failure. During such a slowevolution it would also be anticipated that the frequency of tidalrespirations would increase.

In one embodiment, a motion detection algorithm can also be applied. Thedata set generated by the motion detection comprises a time-seriescomponent of the multi-signal object. If significant motion isidentified at the time of the occurrence of both the tachyarrhythmia andthe loss of the plethesmographic pulse and the motion continues to bepresent, then automatic external cardio version would not go forward andthe device would simply provide a loud auditory and prominent visualalarm. The reason for this adjustment is that motion can in rare casessimulate the presence of a tachyarrhythmia and, further, such motion canresult in loss of a detectable plethesmographic pulse. Rhythmic tappingof the chest wall lead of an electrocardiogram with the same finger towhich the probe of the pulse oximeter is attached, theoretically, couldsimulate the occurrence of pulseless ventricular tachycardia. Inaddition, the development of a chronic seizure, which results insignificant chest wall artifact, as well as rhythmic motion of theextremities could also simulate the development of pulselesstachycardia. For these reasons, according to the present invention, thepresence of significant motion can be used to prevent the processor fromsignaling the controller of the automatic external cardio version devicefrom shocking the patient.

According to another aspect of the present disclosure, a change in oneor more time-series components of the multi-signal object can be used tochange the processing algorithm of a time-series component of themulti-signal object. In an example, the recognition of airwayinstability is enhanced by improved fidelity of the timed waveform (aswith pulse oximetry). FIG. 16 shows one method in accordance withpresent embodiments of improving the general fidelity of the entiretimed waveform of SPO2 for enhanced pattern cluster recognition in anenvironment where the patient, at times, has motion and, at other times,does not. It is optimal, for example, in monitoring oximetry for theprobe to be placed on a portion of the patient, which is not associatedwith motion. However, in most cases, this is unrealistic and motion iscommonly associated with routine clinical oximetry monitoring. It iswell known that motion results in a fall in the saturation value, whichis generated by the oximeter. Multiple theories for the cause of thefall have been promulgated. Several corporations, including Masimo, andNellcor had developed algorithms, which can be used to mitigate theeffect of motion on the accuracy of the output. However, such algorithmscan include a significant amount of signal averaging, generally fourseconds or more. This can result in significant smoothing of thewaveform and reduces the fidelity of the waveform. Furthermore, itattenuates patterns of minor desaturations, which can be indicative ofairway instability, and clusters of hypopneas associated with variationsin airway resistance. As discussed in the aforementioned patents andpatent application, even minor desaturations when occurring in clusterscan be strong evidence for airway or ventilation instability and it isimportant to recognize such desaturations. Unfortunately, averagingintervals, especially those exceeding four seconds or more can result inattenuation of these desaturations and, therefore, hide these clustersso that the airway instability may not be recognized. However, motionitself results in artifact, which can simulate desaturations. Althoughsuch artifact is not expected to occur in typical cluster pattern, thepresence of motion artifacts significantly reduces the value of thesignal as an index of oxygen saturation and airway instability. Thepresent disclosure thereby provides for more optimal continuous fidelityof the waveform through both motion and non-motion states. Asillustrated in FIG. 16, when the motion time-series output suggests thatsubstantial motion is not present, such as deep sleep or sedation,wherein the extremity is not moving, long averaging smoothing algorithmsor motion mitigation algorithms are not applied to the oxygen saturationand plethesmographic pulse time-series. In the alternative, if theseries indicates motion then these motion mitigation algorithms areapplied. The variable application of averaging based on identificationof the absence or presence of motion provides optimal fidelity of thewaveform for monitoring airway instability.

Those skilled in the art will recognize that, the information providedfrom the data and analysis generated from the above-described system canform the basis for other hardware and/or software systems and has widepotential utility. Devices and/or software can provide input to or actas a consumer of the physiologic signal processing system of the presentinvention's data and analysis.

The following are examples of ways that the present physiologic signalprocessing system can interact with other hardware or software systems:

-   -   1. Software systems can produce data in the form of a waveform        that can be consumed by the physiologic signal processing        system.    -   2. Embedded systems in hardware devices can produce a real-time        stream of data to be consumed by the physiologic signal        processing system.    -   3. Software systems can access the physiologic signal processing        system representations of populations of patients for        statistical analysis.    -   4. Software systems can access the physiologic signal processing        system for conditions requiring hardware responses (e.g.        increased pressure in a CPAP device), signal the necessary        adjustment and then analyze the resulting physiological response        through continuous reading of the physiologic signal processing        system data and analysis.

It is anticipated that the physiologic signal processing system will beused in these and many other ways. To facilitate this anticipatedextension through related hardware and software systems the presentsystem will provide an application program interface (API). This API canbe provided through extendable source code objects, programmablecomponents and/or a set of services. Access can be tightly coupledthrough software language mechanisms (e.g. a set of C++ modules or Javaclasses) or proprietary operating system protocols (e.g. Microsoft'sDCOM, OMG's CORBA or the Sun Java Platform) or can be loosely coupledthrough industry standard non-proprietary protocols that providereal-time discovery and invocation (e.g. SOAP [Simple Object AccessProtocol] or WSDL [Web Service Definition Language]).

In one embodiment, the physiologic signal processing system with the APIas defined becomes a set of programmable objects providing afeature-rich development and operating environment for future softwarecreation and hardware integration.

Although embodiments have been described, which relate to the processingof physiologic signals, it is also critical to recognize the presentstreaming parallel objects based data organization and processing methodcan be used to order and analyze a wide range of dynamic patterns ofinteractions across a wide range of corresponding signals and data setsin many environments. The invention is especially applicable to themonitoring of the variations or changes to a physical system, biologicsystem, or machine subjected to a specific process or group of processesover a specific time interval.

The present disclosure provides a new general platform for theorganization and analysis of a very wide range of datasets duringhospitalization or a surgical procedure. For example, in addition to thetime-series of the monitored signals parameters, which may be sampled ata wide range (for example between about 500 hertz and 0.01 hertz),previously noted, the cylindrical data matrix can include a plurality oftime-series of laboratory data, which may be sampled on a daily basis oronly once during the hospitalization. These data points or time-seriesare stored as objects and can be included in the analysis. These objectscan include, for example the results of an echocardiogram wherein atimed value ejection fraction of the left ventricle is provided as anobject in the matrix for comparison with other relationships. Inapplication, the presence of a low ejection fraction object along thematrix with a particular dynamic cyclic variation relationship betweenairflow and oxygen saturation time-series can, for example, providestrong evidence of periodic breathing secondary to congestive heartfailure and this identified relationship can be provided for thehealthcare worker in a textual output. In another example themedications are included in data matrix. For example in a patientreceiving digoxin and furosemide (a diuretic) the daily serum potassiumtime-series is compared to a time-series indicative of the number andseverity of ventricular arrhythmias such as premature ventricularcontractions. A fall in the slope of the potassium time-series inassociation with a rise in slope of such an arrhythmia indicationtime-series could for example produce an output such as “increasedPVCs—possibly secondary to falling potassium, consider checking digoxinlevel”. In another example a first time-series of the total carbondioxide level and a second time-series of the anion gap can be includedin the general streaming object matrix and compared to the time-seriesof airflow. If a rise in the slope or absolute values of the airflow isidentified with a fall in the slope or absolute value along the totalcarbon-dioxide time-series and a rise the slope or absolute values alonethe anion gap time-series, the processor can provide an automaticidentification that the airflow is rising and that the cause of a risein airflow may be secondary to the development of a potentially lifethreatening acidosis, providing an output such as“hyperventilation—possibly due to evolving anion gap acidosis”. Inanother example, the daily weight or net fluid balance is included withthe total carbon dioxide and anion gap in the cylindrical data matrix.The identification of a fall in slope of airflow or absolute value alongthe associated with a fall in slope of the oxygen saturation, and a fallin slope of the fluid balance and weight can generated a output such as“possible hypoventilation-consider contraction alkalosis”.

Alternatively with a matrix made up of the same parameters, a rise inthe slope or absolute values of the airflow time-series and a rise inthe pulse time-series may be recognized in comparison with a fall in thetime-series of the total carbon dioxide, a flat slope of the time-seriesof the anion gap, and a rise in the slope or absolute values of thefluid balance time-series, confirmed by a trending rise in slope of theweight time-series, and a notification can be provided as“hyperventilation—potentially secondary to expansion acidosis orcongestive heart failure”. In one embodiment the cylindrical data matrixbecomes the platform upon which substantially all relevant data derivedduring a hospitalization is stored and processed for discretionary andautomatic comparison. Initial input values, which can be historicalinput, can also be included to set the initial state of the data matrix.For example, if the patient is known to have a history congestive heartfailure, and that is inputted as an initial data point at the start ofthe matrix and that particular conformation in the initial matrix isconsidered in the analysis. The data matrix provides a powerful tool tocompare the onset of dynamic changes in parameters with any externalforce acting on the organism whether this force is pharmacological, aprocedure, related to fluid balance, or even simple transportation toother departments for testing. In one embodiment, as shown in FIG. 1 b,a time-series of action applied to the patient is included called an“exogenous action time-series”. This time-series includes a set ofstreaming objects indicating the actions being applied to the patientthroughout the hospitalization. In this example, within the exogenousaction time-series a time-series component indicative of dynamicoccurrence of a particular invasive procedure, such as the performanceof bronchoscopy, is included. This “bronchoscopic procedure object” may,for example, comprise a time-series component along the exogenous actiontime-series of 15 minutes within the total matrix derived from thehospitalization. The dynamic relationships of the parameters along thematrix are compared with the onset of the procedure (which comprising anobject onset), dynamic patterns of interaction evolving subsequent tothe onset of the procedure can be identified and the temporalrelationship to the procedure object identified and outputted in asimilar manner as has been described above for other objects. Thedynamic patterns of interaction can be interpreted with consideration ofthe type of procedure applied. For example, after a 15 minutetime-series associated with a bronchoscopic procedure, the occurrence ofa progressive increase in slope of the airflow time-series associatedwith a significant decrease in the slope of the inspiration toexpiration slope ratio time-series suggests the development ofbronchospasm secondary to the bronchoscopy and can initiate an outputsuch as “hyperventilation post-bronchoscopy with decreased I:E—considerbronchoscopy”. A larger surgical procedure comprises a longercylindrical data matrix and this can comprise a perioperative matrix,which can include the portion of time beginning with the administrationof the first preoperative medication so that dynamic patterns ofinteraction are compared with consideration of the perioperative periodas a global time-series object within the matrix, with the preoperativeperiod, the operative period, and the post operative period representingtime-series segment of the matrix within the total hospital matrix.Using this objects based relational approach a “dynamic pattern” ofinteraction occurring within this procedure related data stream orsubsequent to it can be easily recognized and temporally correlated withthe procedure so that the dynamic relationships between a procedure andplurality of monitored time-series outputs and/or laboratory data arestored, analyzed, and outputted. In another example, the continuous orintermittent infusion of a pharmaceutical such as a sedative, narcotic,or inotropic drug comprises a time-series which has as one of its timedcharacteristics the dose administered. This new time-series is added tothe cylindrical matrix and the dynamic relationships between monitoredsignals and laboratory data is compared. For example, after theinitiation of Dobutamine (an inotropic drug) the occurrence of a risingslope of pulse rate or a risings slope of premature ventricularcontraction frequency, or the occurrence of an object of non-sustainedventricular tachycardia, can be recognized in relation to onset thetime-series of medication infusion or a particular rise in the slope orabsolute value of the of the dose of this medication. In another examplethe occurrence of a dynamic clustering of apneas such as those presentedin FIGS. 10, 11, and 5 c in relation to a rise in slope, or a particularabsolute value, of the time-series of the sedative infusion can beidentified and the pump can be automatically locked out to preventfurther infusion and an output such as “Caution—pattern suggestive ofmild upper airway instability at dose of 1 mg Versed.” If in thisexample the nurse increases the doe to 2 mg and the pattern shows anincrease in severity an output such as “Pattern suggestive of moderatedupper airway instability at dose of 2 mg/hr. of Versed-dose locked out”.To maintain Versed dose at the 2 mg, level in this patient the nurse orphysician would have to override the lockout. Upon an override theprocessor then tracks the severity of the clusters and if the clustersreach a additional severity threshold then an output such as “Severeupper airway instability—Versed locked out”.

The anticipated range of time-series for incorporation into thecylindrical relational matrix of streaming objects include: multiplepharmaceutical time-series, exogenous action time-series, monitoredsignal time-series (which can include virtually any monitored parameteror its derivative), fluid balance, weight, and temperature time-series,and time-series or single timed data points of laboratory values(including chemistry, hematology, drug level monitoring, and procedurebased outputs (such as echocardiogram and pulmonary function testoutputs). Interpreted radiology results may also be incorporated as datapoints and once the digital signal for such testing can be reasonablysummarized to produce a time-series, which reliably reflects a trend(such as the degree of pulmonary congestion), such outputs can also beinclude in the data matrix as time-series for comparison with, forexample, the net fluid balance and weight time-series. An additionaltime-series can be the provided by nursing input, for example atime-series of the pain index, or Ramsey Scale based level of sedation.This time-series can be correlated with other monitored indices ofsedation or anesthesia as is known in the art.

The cylindrical matrix of processed, analyzed, and objectified dataprovides an optimal new tool for the purpose of doing business todetermine, much more exactly, the dynamic factors, occurrences, andpatterns of relationships, which increase expense in any timed process.In the example of the hospital system discussed supra, the expense datais structured as a time-series of objects with the data point valuerepresented by the total expense at each point. Expense values can belinked and or derived from certain procedures or laboratory tests, forexample the time-series of the hemoglobin can be associated with acorresponding time-series of the calculated expense for that test. Inthe preferred embodiment the plurality of time-series of expense foreach monitored laboratory tests are combined to produce a global expensetime-series. Individual time-series for the expense of each class ofexogenous actions (such as pharmaceutical, and procedural time-series)is also provided and can then be combined to form one global expensetime-series. This is incorporated into the cylindrical data matrix toprovide discretionary comparison with dynamic expense variables anddynamic patterns of relationships of other variables. This allows thehospital to determine the immediate expense related to the occurrence ofan episode of ventricular fibrillation. This expense can be correlatedwith, for example, the timeliness of treatment, the application ofdifferent technologies, or the presence of a specific dynamic pattern ofinteraction of the signals. In other words the immediate cost, andresources expended over, for example, the 24 hours following the episodeof ventricular fibrillation, can be compared with the true behavior andduration of the pathophysiologic components relating the ventricularfibrillation episode.

In a further example, consider a patient monitored with an embodiment ofthe present disclosure deriving a cylindrical data matrix comprised ofstreaming and overlapping objects of airflow, chest wall impedance, EKG,oximetry, and global expense. The occurrence of the procedure forinsertion of the central line represents an object (which need not havea variable value) along a segment of the cylinder. If the patientdevelops a pneumothorax the processor can early identify and warn of thedevelopment of pathophysiologic divergence with respect to the airflow(and/or chest wall impedance) and the oxygen saturation (and/or pulse).In addition to earlier recognition, the expense related to thiscomplication, the timeliness of intervention, the magnitude ofpathophysiologic perturbation due to the complication, and the resourcesexpended to correct the complication can all be readily determined usingthe processor method and data structure of the present disclosure.

Many other additional new component cylinders may be added to thematrix. During the implementation of embodiments of the presentdisclosure, it is anticipated that many subtle relationships between themany components will become evident to those skilled in the art andthese are included within the scope of this disclosure. Those skilled inthe art will recognize that various changes and modifications can bemade without departing from the disclosure. While the presentembodiments have been described in connection with what is presentlyconsidered to be the most practical and preferred embodiments, it is tobe understood that the disclosure is not to be limited to the disclosedembodiments, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

1. A medical alarm system configured to process at least physiologic andlaboratory data of at least one patient, comprising: a computerprogrammed with executable instructions to: identify positive and/ornegative trends comprising at least a combination of inflammatoryindicator trends with one or more of a metabolic trend, a pulse trend, ablood pressure trend, an oxygen saturation trend, and a ventilationtrend; identify a relational pattern of the positive and/or negativetrends which is characteristic of one or more of sepsis, severe sepsis,septic shock, a shock cascade, and a septic shock cascade; and output analarm in response the relational pattern.
 2. The medical alarm system ofclaim 1, wherein the computer is programmed with executable instructionsto: process timed data indicative of patient treatment; identify anonset of the patient treatment; and identify a timing of the patienttreatment in relation to at least one component of the relationalpattern.
 3. The medical alarm system of claim 1, wherein the computer isprogrammed with executable instructions to: detect at least a firsttrend comprising a rise in at least one inflammatory marker, and asecond trend comprising at least one of a fall in bicarbonate and a risein anion gap.
 4. The medical alarm system of claim 3, wherein thecomputer is programmed with executable instructions to detect at least athird trend comprising at a rise in heart rate.
 5. The medical alarmsystem of claim 1, wherein the computer is programmed with executableinstructions to determine and output at least one indication of a typeof the relational pattern.
 6. The medical alarm system of claim 1,wherein the computer is programmed with executable instructions todetermine and output at least an indication of timing and types of thepositive and/or negative trends along the relational pattern.
 7. Themedical alarm system of claim 1, wherein the computer is programmed withexecutable instructions to determine and output a length of therelational pattern.
 8. The medical alarm system of claim 1, wherein thecomputer is programmed with executable instructions to process timeddata indicative of at least one medical procedure and to determine andoutput at least an indication of timing of the at least one medicalprocedure in relation to the relational pattern.
 9. The medical alarmsystem of claim 1, wherein the computer is programmed with executableinstructions to determine and output at least a resource utilizationassociated with the relational pattern.
 10. A medical alarm system forprocessing at least physiologic and laboratory data of at least onepatient comprising a computer programmed with executable instructionsto: detect trends along the data; detect at least one progressivelyexpanding pattern comprised of at least three trends; and output anwarning upon the detection of the at least one progressively expandingpattern.
 11. The medical alarm system of claim 10, wherein the at leastone progressively expanding pattern comprises a cascade of one or moreof sepsis, severe sepsis, septic shock, microcirculatory failure, andshock.
 12. The medical alarm system of claim 11, wherein the cascade iscomprised of a progressively enlarging aggregation of progressivelygreater numbers of perturbed physiologic and laboratory data.
 13. Amedical alarm system for processing at least physiologic and laboratorydata of at least one patient comprising a computer programmed withexecutable instructions to: convert the physiologic and laboratory datainto trends comprised of at least positive and negative trends of boththe physiologic data and the laboratory data; detect relational trendscomprised of a combination of the positive and/or negative trends;detect at least one progressively expanding pattern comprised of aplurality of combinations of the relational trends; and output an alarmindicating the detection of the at least one progressively expandingpattern.
 14. The medical alarm system of claim 13, wherein the at leastone progressively expanding pattern is a progressively enlarging patterncharacteristic of at least one of sepsis, severe sepsis, septic shock,and microcirculatory failure, a shock cascade, and a septic shockcascade.
 15. The medical alarm system of claim 13, wherein the computeris programmed with executable instructions to determine and output atleast one indication of a type of the at least one progressivelyexpanding pattern.
 16. The medical alarm system of claim 13, wherein thecomputer is programmed with executable instructions to determine andoutput an indication of a timing and type of the relational trends alongthe at least one progressively expanding pattern.
 17. The medical alarmsystem of claim 13, wherein the computer is programmed with executableinstructions to determine a length of the at least one progressivelyexpanding pattern.
 18. The medical alarm system of claim 13, wherein thecomputer is programmed with executable instructions to process timeddata indicative of patient treatment and to output at least anindication of timing of treatment in relation to the at least oneprogressively expanding pattern.
 19. A medical alarm system forprocessing at least physiologic and laboratory data of at least onepatient comprising a computer programmed with executable instructionsto: search the physiologic and laboratory data to detect timed trendscomprised of at least positive and negative trends of both thephysiologic data and the laboratory data; convert the physiologic andlaboratory data into a matrix of at least positive and negative trends;determine relational timing of the detected positive and negative trendsalong the matrix; detect at least one pattern along the matrix comprisedof a plurality of combinations of positive and/or negative trendsevolving in timed relation to each other; and output an alarm upon thedetection of the at least one pattern.
 20. The medical alarm system ofclaim 19, wherein the at least one pattern is representative of at leastone of sepsis, severe sepsis, septic shock, a shock cascade, and aseptic shock cascade.
 21. A medical alarm system for warning healthcareworkers and for processing at least physiologic and laboratory data ofat least one patient comprising a computer programmed with executableinstructions to: identify positive and/or negative trends comprising atleast a combination of inflammatory indicator trends with at least oneof, a metabolic trend, pulse trend, blood pressure trend, oxygensaturation trend, and ventilation trend; identify a relational patternof the positive and/or negative trends which are indicative ofprogression of sepsis to a more life threatening state; and output analarm in response to identification of the relational pattern.
 22. Amedical alarm system for warning healthcare workers and for processingat least physiologic and laboratory data of at least one patientcomprising a computer programmed with executable instructions to:convert at least the physiologic and laboratory data into apredetermined format favorable for searching; searching the physiologicand laboratory data in the predetermined format; detecting aprogressively expanding pattern of abnormal data characteristics of atleast sepsis and/or septic shock; and outputting an alarm in response todetection of the progressively expanding pattern.
 23. The medical alarmsystem of claim 22, wherein the predetermined format is comprised oftrends.
 24. The medical alarm system of claim 22, wherein thepredetermined format is a time-series matrix.
 25. The medical alarmsystem of claim 22, wherein the predetermined format is a time-seriesmatrix of trends.
 26. The medical alarm system of claim 22, wherein thepredetermined format is an objectified time-series matrix.
 27. Themedical alarm system of claim 22, wherein the predetermined format is atime-series matrix of objectified trends.